✨SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations

📝 Summary:
SCAIL is a framework that improves character animation to studio-grade quality. It uses a novel 3D pose representation and a diffusion-transformer with full-context pose injection, achieving state-of-the-art realism and reliability.

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05905
• PDF: https://arxiv.org/pdf/2512.05905

🔹 Models citing this paper:
• https://huggingface.co/zai-org/SCAIL-Preview

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✓ https://xn--r1a.website/DataScienceT

#CharacterAnimation #AI #3DAnimation #DeepLearning #ComputerGraphics

✨One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer

📝 Summary:
One-to-All Animation is a unified framework for high-fidelity character animation and image pose transfer. It tackles misaligned and partially visible references using self-supervised outpainting, a robust reference extractor, and identity-robust pose control to outperform existing methods.

🔹 Publication Date: Published on Nov 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22940
• PDF: https://arxiv.org/pdf/2511.22940
• Project Page: https://ssj9596.github.io/one-to-all-animation-project/
• Github: https://github.com/ssj9596/One-to-All-Animation

🔹 Models citing this paper:
• https://huggingface.co/MochunniaN1/One-to-All-14b
• https://huggingface.co/MochunniaN1/One-to-All-1.3b_2
• https://huggingface.co/MochunniaN1/One-to-All-1.3b_1

✨ Datasets citing this paper:
• https://huggingface.co/datasets/MochunniaN1/One-to-All-sub

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#CharacterAnimation #PoseTransfer #ComputerVision #AI #DeepLearning

✨Animate Any Character in Any World

📝 Summary:
AniX extends controllable-entity models to enable diverse, user-defined character interactions in static 3D environments via natural language. It synthesizes temporally coherent videos through conditional autoregressive video generation, allowing characters to perform open-ended actions.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17796
• PDF: https://arxiv.org/pdf/2512.17796
• Project Page: https://snowflakewang.github.io/AniX/
• Github: https://github.com/snowflakewang/AniX

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✓ https://xn--r1a.website/DataScienceT

#GenerativeAI #VideoGeneration #CharacterAnimation #NLP #3D

✨DreamActor-M2: Universal Character Image Animation via Spatiotemporal In-Context Learning

📝 Summary:
DreamActor-M2 is a universal character animation framework. It uses in-context learning to fuse appearance and motion cues, along with self-bootstrapped data synthesis for RGB-driven animation. This approach overcomes motion injection tradeoffs and pose prior limitations, achieving superior fidel...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21716
• PDF: https://arxiv.org/pdf/2601.21716
• Project Page: https://grisoon.github.io/DreamActor-M2/
• Github: https://grisoon.github.io/DreamActor-M2/

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✓ https://xn--r1a.website/DataScienceT

#CharacterAnimation #AI #ComputerVision #DeepLearning #GenerativeAI

🔥 SCAIL-2: Unifying Controlled Character Animation with End-to-end In-Context Conditioning

💡 The paper presents SCAIL-2, a framework for controlled character animation that enables end-to-end motion transfer from driving videos to reference characters without using intermediate representations. Prior methods relied on intermediate representations such as pose skeletons or masked backgrounds, which led to information loss. SCAIL-2 addresses this issue by directly concatenating driving videos to the sequence, allowing the model to obtain all required visual information from the input video.

To overcome the lack of end-to-end data, the authors unify sub-tasks of character animation with decoupled conditions and create a pipeline to synthesize a large dataset called MotionPair-60K, which contains heterogeneous tasks of character animation. The framework utilizes in-context mask conditioning and mode-specific RoPE as soft guidance beyond textual instructions and raw visual information.

The authors also propose Bias-Aware DPO to mitigate errors caused by synthetic discrepancies in detailed regions. This approach constructs preference items to address the issue. Extensive experiments demonstrate that SCAIL-2 substantially outperforms existing state-of-the-art approaches in various character animation tasks.

The key contributions of the paper are the development of an end-to-end character animation framework that bypasses intermediate representations, the creation of a large synthetic dataset for motion transfer, and the proposal of a novel method to address synthetic discrepancies. The results show that SCAIL-2 achieves superior performance compared to existing methods, and the authors plan to release a large subset of synthetic data and model weights to facilitate further research.

📅 Published on Jun 9

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.10804
• PDF: https://arxiv.org/pdf/2606.10804
• Project Page: https://teal024.github.io/SCAIL-2/

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📢 By: https://xn--r1a.website/PaperNexus

#CharacterAnimation #MotionTransfer #EndToEndLearning #InContextConditioning #ComputerVision