Pith. sign in

REVIEW 9 cited by

StableAnimator: High-Quality Identity-Preserving Human Image Animation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.17697 v2 pith:TBRSO6JF submitted 2024-11-26 cs.CV cs.AI

Shuyuan Tu , Zhen Xing , Xintong Han , Zhi-Qi Cheng , Qi Dai , Chong Luo , Zuxuan Wu This is my paper
classification cs.CVcs.AI
keywords stableanimatorimagediffusionfaceembeddingsanimationconsistencydenoising
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Current diffusion models for human image animation struggle to ensure identity (ID) consistency. This paper presents StableAnimator, the first end-to-end ID-preserving video diffusion framework, which synthesizes high-quality videos without any post-processing, conditioned on a reference image and a sequence of poses. Building upon a video diffusion model, StableAnimator contains carefully designed modules for both training and inference striving for identity consistency. In particular, StableAnimator begins by computing image and face embeddings with off-the-shelf extractors, respectively and face embeddings are further refined by interacting with image embeddings using a global content-aware Face Encoder. Then, StableAnimator introduces a novel distribution-aware ID Adapter that prevents interference caused by temporal layers while preserving ID via alignment. During inference, we propose a novel Hamilton-Jacobi-Bellman (HJB) equation-based optimization to further enhance the face quality. We demonstrate that solving the HJB equation can be integrated into the diffusion denoising process, and the resulting solution constrains the denoising path and thus benefits ID preservation. Experiments on multiple benchmarks show the effectiveness of StableAnimator both qualitatively and quantitatively.

Discussion (0). Sign in to comment.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AgentHOI: Multi-Agent Reasoning for Human-Object-Interaction Video Generation via Implicit Representation Alignment

    cs.CV 2026-07 conditional novelty 6.0 of 10

    AgentHOI generates human-object interaction videos from text plus one human image and one object image, using multi-agent action planning and implicit text-to-motion feature alignment inside a video diffusion model.

  2. PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image

    cs.CV 2025-08 conditional novelty 6.0 of 10

    PERSONA creates a personalized 3D avatar from one image by using diffusion-generated pose-rich videos to train a 3D Gaussian avatar with balanced sampling and geometry-weighted optimization.

  3. DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A diffusion transformer model generates human-product demonstration videos from paired human and product images while preserving both identities through masked cross-attention and motion template guidance.

  4. HunyuanVideo-HOMA: Generic Human-Object Interaction in Multimodal Driven Human Animation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    HunyuanVideo-HOMA generates human-object interaction videos from weak, sparse inputs: one arm pose, an object center dot, a human photo, and an object photo.

  5. StableAnimator++: Overcoming Pose Misalignment and Face Distortion for Human Image Animation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    StableAnimator++ combines learnable SVD-guided pose alignment, a distribution-aware ID Adapter, and an HJB-based inference-time face optimizer to preserve identity in human image animation under severe pose misalignment.

  6. Human Motion Video Generation: A Survey

    cs.CV 2025-09 conditional novelty 4.0 of 10

    A comprehensive survey with a five-phase pipeline model for human motion video generation, covering over 200 papers and adding a new benchmark comparison of nine pose-guided methods.

  7. Preview WB-DH: Towards Whole Body Digital Human Bench for the Generation of Whole-body Talking Avatar Videos

    cs.CV 2025-08 reject novelty 4.0 of 10

    The paper previews a claimed 2M-clip multimodal benchmark for whole-body talking avatar video generation, with standard metrics and an initial evaluation of eight open-source models.

  8. JWB-DH-V1: Benchmark for Joint Whole-Body Talking Avatar and Speech Generation Version 1

    cs.CV 2025-07 reject novelty 4.0 of 10

    A paper announcing a large-scale whole-body talking avatar benchmark and evaluation protocol, but with insufficient details to verify the dataset or the joint audio-video evaluation.

  9. EgoAnimate: Generating Human Animations from Egocentric top-down Views

    cs.CV 2025-07 conditional novelty 4.0 of 10

    EgoAnimate synthesizes a frontal T-pose image from an egocentric top-down photo using a fine-tuned Stable Diffusion model, then animates it with off-the-shelf image-to-motion methods to produce an animatable avatar.

Pith tools