KlingAvatar 2.0 Technical Report

🍞 Hook: Imagine you’re directing a school play on video. You want the actors to speak clearly, show the right emotions, move naturally, and follow the script and camera directions. Now imagine doing that for a five-minute movie, without anyone forgetting their lines or drifting off-topic.

🥬 The Story So Far: Audio-driven avatar video synthesis means making a realistic talking person video from a single photo or few images and an audio track, sometimes guided by text and camera instructions. Early systems were like lip-sync karaoke: they focused on matching mouth shapes to sounds. That was great for short, close-up clips, but limited for full-body moves, camera motion, and longer, richer stories.

As diffusion transformers (DiT) for images and videos grew strong, researchers added bodies, hands, backgrounds, and even camera paths. These models could generate impressive short clips. But when people tried to go long (tens of seconds to minutes) and sharp (high resolution), three big gremlins crept in: • Temporal drifting: the video slowly ‘forgets’ what’s happening—faces morph a bit, camera paths wobble, or actions lose consistency. • Quality degradation: details like hair strands, teeth, and skin texture get blurry or flickery over time. • Weak prompt follow-up: when instructions are complex (mixing audio emotion, visual style, and text camera commands), the video starts ignoring key directions as it goes longer.

🍞 Anchor: Think of trying to write a whole comic book in one go without planning panels first. The longer you go, the easier it is to lose track of who’s where and what they’re doing.

— New Concept — 🍞 Hook: You know how, when a friend tells a long story without notes, they start to ramble? 🥬 The Concept: Temporal drifting is when a long video gradually loses track of identities, positions, or actions as frames go by. How it works: (1) Small frame-to-frame errors add up; (2) The model’s memory of the original plan fades; (3) Visual and motion details slowly wander. Why it matters: Without fixing it, long videos look inconsistent and confusing. 🍞 Anchor: It’s like playing telephone for 300 frames—by the end, the message changes.

— New Concept — 🍞 Hook: Imagine a puppet show where the music controls the puppets’ mouths and gestures. 🥬 The Concept: Audio-driven avatar synthesis makes a person in a video move, speak, and emote based on an audio track. How it works: (1) Convert audio to features (words, rhythm, emotion); (2) Guide facial and body motion; (3) Render frames that match the sound. Why it matters: It’s the heart of talking-person videos—no good audio link, no believable speaking. 🍞 Anchor: When the audio says “Wow!”, the avatar’s mouth forms “Wow” and the eyebrows pop.

People tried several fixes before. Some methods used landmarks (dots for mouth and face) to keep control; others trained bigger models with more data; some added text planners to layout shots. These helped but didn’t fully solve long, sharp, instruction-heavy videos. The missing piece was a smarter process: plan first across all modalities (audio, image, text), then build videos in steps that protect both spatial detail and temporal memory.

The real stakes are everyday: teachers who want long, expressive lectures with a friendly guide; training videos that show the right safety steps; multi-host podcasts turned into engaging visuals; or movie-like ads where the camera swoops exactly as written. If the avatar drifts, glitches, or ignores instructions, the spell breaks.

KlingAvatar 2.0 fills this gap with two big ideas: (1) a Co-Reasoning Director to plan and align audio, images, and text into a clear shot-by-shot storyline (with both positive and negative prompts); and (2) a spatio-temporal cascade that first makes a low-res ‘blueprint’ and then upgrades it in space (resolution) and time (length), stitching sub-clips with first–last frame guidance to keep everything consistent.

— New Concept — 🍞 Hook: You know how a coach, a choreographer, and a scriptwriter together make a great performance? 🥬 The Concept: A Co-Reasoning Director is a team of expert AIs (audio, vision, text) that discuss and plan a clear, conflict-free storyline. How it works: (1) Each expert analyzes its modality; (2) They chat to resolve conflicts (angry tone vs. cheerful script); (3) They output a step-by-step shot plan. Why it matters: Without a solid plan, the model misunderstands long, mixed instructions. 🍞 Anchor: If the script says “camera pans up” while the audio is calm and slow, the director aligns timing so both match.

— New Concept — 🍞 Hook: Think of drawing a rough sketch before painting all the colors. 🥬 The Concept: A spatio-temporal cascade builds videos in stages—first low-res keyframes to capture the story, then higher-res and longer clips for details and smooth flow. How it works: (1) Make a low-res blueprint; (2) Upscale keyframes; (3) Expand to sub-clips with first–last frame anchors; (4) Super-res the final video. Why it matters: It stops drift, keeps details crisp, and saves compute. 🍞 Anchor: Like building Lego: base plate first, then layers, then decorations.

Together, these ideas let KlingAvatar 2.0 create longer, clearer, more obedient avatar videos that match the sounds, the scripts, and the visuals—without wandering off or getting fuzzy.

🍞 Hook: Imagine filming a long school movie by first writing a smart shot list with help from three experts, then filming quick thumbnails of each scene, and finally coloring them in HD—making sure each scene starts and ends exactly where it should.

🥬 The Aha in One Sentence: Plan across audio–image–text first, then generate videos in a step-by-step spatial–temporal cascade so long, high-res avatar videos stay sharp, on-script, and emotionally in sync.

Three Analogies:

1. Comic-to-Film Pipeline: Sketch the comic panels (blueprint keyframes), then animate them (sub-clips), then polish in 4K (super-resolution), all following a script written by three editors (audio, image, text experts).
2. Cooking a Feast: The head chef (director) plans dishes and timing; you par-cook basics (blueprints), finish each dish perfectly (refine sub-clips), then plate beautifully (super-res), making sure courses connect.
3. Orchestra with a Conductor Team: Audio = rhythm section, Text = sheet music, Image = stage setting; co-conductors keep tempo, notes, and lighting aligned; the piece is rehearsed in small ensembles, then performed in full.

Before vs After: • Before: Models tried to do everything at once—leading to drift, fuzziness, and missed instructions as videos got longer. • After: A clear director plan plus staged video building keeps identity, camera motion, lip–teeth details, and emotions consistent across minutes.

Why It Works (Intuition): • Planning cuts confusion: Multimodal conflicts get solved up front (e.g., voice says ‘sad’ while text says ‘smile slowly’). The director reconciles timing and emotion so the backbone model doesn’t guess. • Anchors tame time: First–last frame guidance acts like story beats—each sub-clip starts and ends at agreed poses, so errors don’t snowball. • Cascades protect detail and coherence: Low-res stages capture the big picture cheaply; later stages add detail without rewriting the story. • Negative prompts act like guardrails: By saying what not to do (e.g., ‘don’t over-open the mouth’), the model avoids common pitfalls shot-by-shot.

Building Blocks (with mini-Sandwiches): — Co-Reasoning Director — 🍞 Hook: Like three teachers comparing notes before parent night. 🥬 The Concept: Three expert AIs (audio, visual, text) discuss and draft a unified, conflict-free, shot-level plan. How it works: analyze → debate → decide → output positive and negative prompts. Why it matters: Avoids mixed signals and missing details. 🍞 Anchor: It lines up ‘soft, sad voice’ with ‘dim lighting’ and ‘slow camera tilt’ in the same shot.

— Multimodal Instruction Alignment — 🍞 Hook: A band needs to play the same song, same tempo. 🥬 The Concept: Keep audio cues, text commands, and image references in harmony across the whole video. How it works: director fuses priorities; diffusion follows that plan per shot. Why it matters: Prevents the video from ignoring parts of the prompt. 🍞 Anchor: When text says ‘pan up’ exactly when the chorus hits, the camera moves on the beat.

— Spatio-Temporal Cascade — 🍞 Hook: Build the sandcastle’s shape before carving seashell details. 🥬 The Concept: Generate a low-res blueprint, upscale keyframes, expand to sub-clips with first–last anchors, then super-res the final. How it works: global-first, local-next; coarse-to-fine. Why it matters: Stops drift, keeps identity and details crisp. 🍞 Anchor: You won’t mistake the main character after upscaling; the face stays the same.

— First–Last Frame Conditioning — 🍞 Hook: A relay race works because the baton handoffs match. 🥬 The Concept: Each sub-clip is guided by the starting and ending frames to lock continuity. How it works: condition generation on both frames; interpolate audio-aware motion inside. Why it matters: Sub-clips connect smoothly; no sudden jumps. 🍞 Anchor: The hand that starts halfway raised ends fully raised exactly when planned.

— Negative Director — 🍞 Hook: A recipe might say ‘don’t overbake.’ 🥬 The Concept: Shot-specific ‘don’t do this’ prompts (e.g., avoid over-wide mouth, jittery hands) prevent common errors. How it works: co-reasoning outputs per-shot negatives alongside positives. Why it matters: Less flicker, fewer wrong emotions, cleaner details. 🍞 Anchor: During a quiet verse, it prohibits ‘fast head bobbing.’

— Multi-Character Control — 🍞 Hook: Like a coach telling each player their moves. 🥬 The Concept: Predict masks for each person and inject each audio only where that person is. How it works: deep DiT features predict per-character regions; gate audio features by mask. Why it matters: Voices don’t cross; each avatar follows their own audio. 🍞 Anchor: Person A laughs while Person B nods to their own line—no mix-ups.

Put together, KlingAvatar 2.0 plans well, builds carefully, and guards against mistakes—so long, high-res, multi-person avatar videos stay believable and on-script.

High-Level Recipe: Inputs (reference image(s), audio track(s), text instructions) → Co-Reasoning Director (positive/negative shot plan) → Low-res blueprint keyframes → High-res keyframes → Low-res sub-clips via first–last conditioning + audio-aware interpolation → High-res sub-clips (super-resolution) → Concatenate into long, coherent video.

Step-by-Step (what/why/example):

1. Co-Reasoning Director: multimodal planning • What: Three experts analyze inputs: audio expert (transcript, emotion, rhythm), visual expert (identity, clothing, background), text expert (script, camera cues). They debate in multiple turns, then output a structured, shot-by-shot plan with both positive and negative prompts. • Why: Without a unified plan, the generator faces conflicting signals (e.g., ‘angry tone’ vs. ‘smile warmly’), causing missed instructions and drift later. • Example: Audio says ‘soft, rising excitement’; text says ‘pan up as she raises her hands’; image shows a bright room. The plan sets Shot 2: ‘2.5s slow pan up, hands rising to chest, gentle smile; negative: avoid sudden head jerks, avoid over-wide mouth.’

2. Low-Resolution Blueprint Video and Keyframes • What: A fast, low-res DiT video diffusion model creates a rough blueprint capturing global story beats, motion flow, and layouts. Representative keyframes are selected. • Why: Cheaply locks the big picture—who, where, and main actions—before spending compute on details. • Example: The blueprint shows the character turning to face front by second 3 and starting a slow hand fold by second 4.

3. High-Resolution Keyframe Upscaling • What: A high-res DiT refines selected keyframes, boosting identity fidelity (face, teeth, hair), textures, and lighting while respecting the director’s prompts. • Why: Keyframes act as high-detail anchors; later steps can reference them to keep visual identity stable. • Example: The character’s hair strands, lip–teeth rendering, and skin texture are sharpened in keyframes at t=0s, t=3s, t=6s.

4. First–Last Frame Conditioned Sub-Clip Generation (Low-Res) • What: For each segment between two high-res keyframes, a low-res DiT expands motion by conditioning on the first and last frames. Inside each segment, audio-aware interpolation aligns motion timing with speech rhythm and emotion. • Why: First–last anchors prevent drift inside the segment; audio-aware interpolation syncs gestures and facial dynamics with the soundtrack. • Example: Between t=3s and t=6s, the right hand rises while the voice crescendos; negatives forbid fast head snaps.

5. High-Resolution Video Super-Resolution for Sub-Clips • What: A high-res video diffusion model super-resolves each sub-clip, preserving temporal consistency and fine details, guided by the same shot prompts (including negatives). • Why: Final polish—sharp textures and stable lighting without losing earlier motion coherence. • Example: Teeth edges remain crisp during a smile; no flicker on cheeks as lighting shifts.

6. Multi-Character, Multi-Audio Control (if multiple speakers) • What: Attach a mask-prediction head to deep DiT features to find where each identity is in each frame. Use these masks to gate the right audio features into the right spatial regions. • Why: Keeps each character’s motions and lip sync tied to their own audio; avoids voice–body mismatches in conversations. • Example: In a debate, Speaker A shakes head while Speaker B smiles; each follows their own audio. Masks ensure their animations don’t bleed into each other.

7. Negative Director in the Loop • What: Alongside positives (desired actions), per-shot negatives specify what to avoid (e.g., ‘no excessive mouth opening,’ ‘avoid wrist jitter,’ ‘no overexposure highlights’), updated as the story evolves. • Why: Shot-specific guardrails reduce artifacts and emotional mismatches, especially in long sequences. • Example: During a calm verse, negatives suppress ‘fast nodding’ and ‘over-bright flash’ artifacts.

8. Acceleration via Trajectory-Preserving Distillation • What: Use distillation (e.g., PCM/DCM-style) to train a faster student that follows the teacher’s denoising trajectory; customize time schedulers where the base model is most robust. • Why: Fewer inference steps with minimal quality loss enables practical long videos. • Example: A 5-minute video renders faster while keeping lip sync and identity stable.

Secret Sauce (why it’s clever): • Dual cascades in both space and time tie the global story to local detail without re-inventing the scene each step. • First–last conditioning turns each sub-clip into a well-anchored chapter, reducing error buildup. • Co-Reasoning with negatives is like having both a coach and a referee—push toward goals while blocking common fouls. • Mask-gated audio injection exploits deep DiT features that naturally separate characters, enabling clean multi-person control.

— New Concept — 🍞 Hook: Think of marking the start and finish lines for each lap in a race. 🥬 The Concept: First–last frame conditioning guides sub-clip generation by fixing its entry and exit frames. How it works: condition diffusion on both frames; synthesize in-between frames that match audio. Why it matters: Ensures smooth transitions and consistent poses. 🍞 Anchor: The hand is guaranteed to be fully folded by the end of the clip—no last-second surprises.

— New Concept — 🍞 Hook: A red ‘X’ on a map shows where not to go. 🥬 The Concept: A Negative Director provides precise ‘don’t do this’ prompts per shot. How it works: co-reasoning identifies likely errors and writes targeted negatives. Why it matters: Cuts artifacts and keeps emotion/motion faithful. 🍞 Anchor: ‘Don’t over-open mouth’ avoids cartoonish vowels.

— New Concept — 🍞 Hook: A loudspeaker should point at the right audience section. 🥬 The Concept: Mask-gated audio injection sends each audio stream only to the pixels of the correct person. How it works: predict masks from deep features; multiply-inject audio features per mask. Why it matters: Prevents voice–body mix-ups in multi-speaker scenes. 🍞 Anchor: The kid’s laugh doesn’t make the teacher’s mouth move.

— New Concept — 🍞 Hook: Study guides let you learn faster while keeping the main ideas. 🥬 The Concept: Trajectory-preserving distillation trains a faster model to follow the teacher’s denoising path. How it works: match intermediate steps; use smart time schedules. Why it matters: Big speedups with small quality trade-offs. 🍞 Anchor: Same A+ answer, fewer steps to get there.

The Test: The team built 300 high-quality cases mixing image, audio, and text prompts—100 Chinese speech, 100 English speech, and 100 singing. Human evaluators compared videos pairwise (Good/Same/Bad, GSB metric) across fine-grained axes: • Face–Lip Synchronization: Are lips and expressions correctly timed to speech? Are lip–teeth details natural? • Visual Quality: Are textures, hair, skin, and lighting sharp and stable over time? • Motion Quality: Are body, head, and camera motions smooth and plausible, without warps or jitter? • Motion Expressiveness: Do gestures and faces feel lively and match the emotion and timing in audio/text? • Text Relevance: Do camera paths, scene changes, and actions truly follow the script?

The Competition: KlingAvatar 2.0 was compared against three strong baselines—HeyGen, Kling-Avatar, and OmniHuman-1.5—well-known systems for talking avatar/video generation.

The Scoreboard (with context): • Overall Preference: KlingAvatar 2.0 beats HeyGen and Kling-Avatar by notable margins, and also tops OmniHuman-1.5 in aggregate GSB comparisons. Think of it like getting an A when others score around B. • Per-Dimension Highlights: The biggest gains appear in motion expressiveness and text relevance—like adding a great actor and a great director at once. Lip sync accuracy and visual detail (especially lip–teeth and hair) are also consistently strong. • Example Stats from the paper’s visualized comparisons: Against HeyGen and Kling-Avatar, KlingAvatar 2.0 shows substantial overall preference gains (e.g., on the order of 25–38%+ in several criteria). Against OmniHuman-1.5, KlingAvatar 2.0 continues to hold advantages in multiple axes, reflecting better adherence to complex instructions and more vivid, stable motion.

Surprising Findings: • Hair Dynamics and Physicality: Baselines sometimes had rigid or less grounded hair motion; KlingAvatar 2.0 produced more physically plausible hair and head movement across time. • Camera Control: Some baselines either played it too safe (simple trajectories) or missed specific camera cues. KlingAvatar 2.0 followed detailed camera instructions (like ‘bottom-to-top pan’) more faithfully. • Fine-Grained Motion Semantics: In a prompt asking to ‘fold hands in front of the chest,’ one baseline folded at the waist. KlingAvatar 2.0 got the action right and synced it with the intended emotion. • Negative Director Ablation: Without shot-specific negatives, faces could show over-large mouth shapes, exposure flicker, or unstable expressions. Adding the Negative Director reduced these errors, improved temporal stability, and enhanced emotional control.

Efficiency and Long-Form Coherence: • The spatial–temporal cascade and distillation reduced compute costs while keeping quality. The system maintained identity consistency and storyline continuity for videos up to around 5 minutes—rare for high-res, multi-instruction settings.

Multi-Character Results: • With mask-gated audio injection, the model produced synchronized, identity-preserving animations for multiple speakers in the same scene. The automated annotation pipeline (YOLO + DWPose + SAM2) enabled large-scale training for reliable mask prediction.

Bottom Line: Across human preference tests and qualitative demos, KlingAvatar 2.0 consistently looks more natural, more expressive, and more obedient to complex, multimodal instructions—especially on long, high-resolution stories.

Limitations: • Data Hunger: The system benefits from large, cinematic-quality datasets, including diverse languages and multi-person scenes. In low-data domains (rare outfits, unusual poses), performance may drop. • Compute Requirements: Despite distillation, high-res, long videos still demand strong GPUs and time, especially when multi-characters and complex camera paths are involved. • Edge Cases: Very fast or erratic motions, heavy occlusions, or wildly fluctuating lighting can still challenge temporal stability. Extremely subtle emotions may require even finer control signals. • Over-Constraint Risk: If negative prompts are mis-specified or too strict, they may dampen expressiveness or cause under-animated behavior.

Required Resources: • Hardware: Modern GPUs with substantial VRAM for training and inference; storage for large video datasets. • Software/Models: Pretrained video DiTs, audio/text encoders, MLLM experts for planning, and segmentation/detection tools (YOLO, DWPose, SAM2) for multi-character data. • Data: High-quality, well-aligned audio–video–text triplets, including multilingual speech and varied emotional performances.

When Not to Use: • Ultra–real-time streaming on very weak devices (latency constraints may be too tight). • Scenarios where perfect factual accuracy is required (this is a generative model; for news broadcasting, live legal testimony, etc., strict verification is needed). • Non-speech tasks that require precise physical simulation beyond training (e.g., complex sports with contact physics).

Open Questions: • Planning Depth: How many turns of co-reasoning are ideal before diminishing returns? Can automatic confidence measures decide when to stop? • Emotion Grounding: How to map nuanced, culture-specific emotional cues from audio/text into universally readable expressions? • Robustness: Can we further reduce drift in extreme motion or lighting, or with crowded multi-person scenes? • Speed–Quality Frontier: Can one-step or few-step diffusion (with improved distillation) reach near-teacher quality at scale for minutes-long videos? • Safety and Attribution: How to watermark outputs, preserve identity consent, and prevent misuse while enabling creative, beneficial applications?

Three-Sentence Summary: KlingAvatar 2.0 plans long avatar videos with a Co-Reasoning Director that fuses audio, image, and text into clear, shot-by-shot positives and negatives. It then builds the video in a spatio-temporal cascade—first a low-res blueprint, then high-res keyframes, sub-clips anchored by first–last frames, and final super-resolution—to keep details sharp and the story consistent. This combination delivers long, high-resolution, multi-character videos with accurate lip–teeth rendering, strong identity preservation, expressive motion, and faithful instruction following.

Main Achievement: Unifying multimodal co-reasoning with a spatial–temporal cascade—and adding shot-specific negative guidance and mask-gated multi-audio control—so long-form, high-res, instruction-heavy avatar videos remain coherent, crisp, and emotionally aligned.

Future Directions: • Faster, stronger distillation for near real-time long-form generation. • Richer emotional and cultural nuance modeling, including micro-expressions and subtle gestures. • More robust multi-character interactions with objects and environments. • Expanded safety features: watermarking, consent verification, and controllable identity protections.

Why Remember This: It shows that planning first (with a multimodal director) and building step-by-step (with spatial–temporal cascades) is the key to stable, vivid, long avatar videos—turning scattered instructions into a believable, movie-like performance that holds together from first frame to last.