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SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformers

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arxiv 2502.10841 v1 pith:VH2EVQOB submitted 2025-02-15 cs.CV

Di Qiu , Zhengcong Fei , Rui Wang , Jialin Bai , Changqian Yu , Mingyuan Fan , Guibin Chen , Xiang Wen This is my paper · ORCID
classification cs.CV
keywords facialidentityskyreels-a1videoanimationmotiondiffusiondiverse
verification ladder T0 review T1 audit T2 compute T3 formal
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We present SkyReels-A1, a simple yet effective framework built upon video diffusion Transformer to facilitate portrait image animation. Existing methodologies still encounter issues, including identity distortion, background instability, and unrealistic facial dynamics, particularly in head-only animation scenarios. Besides, extending to accommodate diverse body proportions usually leads to visual inconsistencies or unnatural articulations. To address these challenges, SkyReels-A1 capitalizes on the strong generative capabilities of video DiT, enhancing facial motion transfer precision, identity retention, and temporal coherence. The system incorporates an expression-aware conditioning module that enables seamless video synthesis driven by expression-guided landmark inputs. Integrating the facial image-text alignment module strengthens the fusion of facial attributes with motion trajectories, reinforcing identity preservation. Additionally, SkyReels-A1 incorporates a multi-stage training paradigm to incrementally refine the correlation between expressions and motion while ensuring stable identity reproduction. Extensive empirical evaluations highlight the model's ability to produce visually coherent and compositionally diverse results, making it highly applicable to domains such as virtual avatars, remote communication, and digital media generation.

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Cited by 10 Pith papers

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

  1. Instant Expressive Gaussian Head Avatars at Over 100 FPS

    cs.CV 2025-12 conditional novelty 7.0 of 10

    A single-photo avatar encoder with per-Gaussian feature-space deformation animates faces at 107 FPS with expression quality competitive with diffusion models.

  2. FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers

    cs.CV 2025-07 conditional novelty 7.0 of 10

    A DiT-based portrait animation model transfers implicit facial expressions to one or more characters using a masked cross-attention mechanism, supported by a new multi-face dataset and benchmark.

  3. Video World Models with Long-term Spatial Memory

    cs.CV 2025-06 conditional novelty 6.0 of 10

    An autoregressive video world model with a persistent static point-cloud spatial memory and sparse episodic keyframes improves revisit consistency over point-cloud-conditioned baselines.

  4. FramePrompt: In-context Controllable Animation with Zero Structural Changes

    cs.GR 2025-06 conditional novelty 5.0 of 10

    FramePrompt turns character animation into a video-continuation task by concatenating reference image, skeleton frames, and target frames into one sequence, then training the pretrained Wan-I2V model to generate only ...

  5. AlignHuman: Improving Motion and Fidelity via Timestep-Segment Preference Optimization for Audio-Driven Human Animation

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Timestep-segment preference optimization with separate motion and fidelity LoRAs improves audio-driven human animation quality and allows a 3.3x inference speedup.

  6. SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers

    cs.CV 2025-06 conditional novelty 5.0 of 10

    An audio-conditioned video diffusion transformer that animates portraits from image, video, text, and audio inputs with a sliding-window fusion for long videos.

  7. FaceEditTalker: Controllable Talking Head Generation with Facial Attribute Editing

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A single framework can edit predefined facial attributes in audio-synchronized talking head videos while preserving identity and lip-sync quality.

  8. Why Fake ? Unveiling the Semantic Vocabulary of Deepfake Detectors

    cs.CV 2026-07 conditional novelty 4.0 of 10

    Applying Encoding-Decoding Direction Pairs to an Xception deepfake detector reveals 16 interpretable concepts (e.g., fake-mouth, real-eyes) that drive real/fake predictions, with concept-level interventions achieving ...

  9. 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.

  10. 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.

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