MTVCrafter: 4D Motion Tokenization for Open-World Human Image Animation
Human image animation has gained increasing attention and developed rapidly
due to its broad applications in digital humans. However, existing methods rely
largely on 2D-rendered pose images for motion guidance, which limits
generalization and discards essential 3D information for open-world animation.
To tackle this problem, we propose MTVCrafter (Motion Tokenization Video
Crafter), the first framework that directly models raw 3D motion sequences
(i.e., 4D motion) for human image animation. Specifically, we introduce 4DMoT
(4D motion tokenizer) to quantize 3D motion sequences into 4D motion tokens.
Compared to 2D-rendered pose images, 4D motion tokens offer more robust
spatio-temporal cues and avoid strict pixel-level alignment between pose image
and character, enabling more flexible and disentangled control. Then, we
introduce MV-DiT (Motion-aware Video DiT). By designing unique motion attention
with 4D positional encodings, MV-DiT can effectively leverage motion tokens as
4D compact yet expressive context for human image animation in the complex 3D
world. Hence, it marks a significant step forward in this field and opens a new
direction for pose-guided human video generation. Experiments show that our
MTVCrafter achieves state-of-the-art results with an FID-VID of 6.98,
surpassing the second-best by 65%. Powered by robust motion tokens, MTVCrafter
also generalizes well to diverse open-world characters (single/multiple,
full/half-body) across various styles and scenarios. Our video demos and code
are on: https://github.com/DINGYANB/MTVCrafter.
682bfefa73f0db9ddd6c73f7
Yanbo Ding
claimed_verified
2025-05-20T07:20:19.748Z
65c09224a9c1b20e69a61569
/avatars/78c73be711f2c7a889acb088507ca0aa.svg
YANBO DING
user
yanboding
682bfefa73f0db9ddd6c73f8
Xirui Hu
682bfefa73f0db9ddd6c73f9
Zhizhi Guo
682bfefa73f0db9ddd6c73fa
Yali Wang
MTVCrafter: 4D Motion Tokenization for Open-World Human Image Animation
Human image animation has gained increasing attention and developed rapidly
due to its broad applications in digital humans. However, existing methods rely
largely on 2D-rendered pose images for motion guidance, which limits
generalization and discards essential 3D information for open-world animation.
To tackle this problem, we propose MTVCrafter (Motion Tokenization Video
Crafter), the first framework that directly models raw 3D motion sequences
(i.e., 4D motion) for human image animation. Specifically, we introduce 4DMoT
(4D motion tokenizer) to quantize 3D motion sequences into 4D motion tokens.
Compared to 2D-rendered pose images, 4D motion tokens offer more robust
spatio-temporal cues and avoid strict pixel-level alignment between pose image
and character, enabling more flexible and disentangled control. Then, we
introduce MV-DiT (Motion-aware Video DiT). By designing unique motion attention
with 4D positional encodings, MV-DiT can effectively leverage motion tokens as
4D compact yet expressive context for human image animation in the complex 3D
world. Hence, it marks a significant step forward in this field and opens a new
direction for pose-guided human video generation. Experiments show that our
MTVCrafter achieves state-of-the-art results with an FID-VID of 6.98,
surpassing the second-best by 65%. Powered by robust motion tokens, MTVCrafter
also generalizes well to diverse open-world characters (single/multiple,
full/half-body) across various styles and scenarios. Our video demos and code
are on: https://github.com/DINGYANB/MTVCrafter.
MTVCrafter, a framework using 4D motion tokens and motion-aware Multi-view DiT, significantly improves human image animation by modeling raw 3D motion sequences.
4D motion tokenizer 4D motion tokens MV-DiT motion attention 4D positional encodings FID-VID open-world animation human video generation