Helix4D: Complex 4D Mesh Generation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914600263352320 |
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| author | Yenphraphai, Jiraphon Chen, Jianqi Wang, Jian Qian, Gordon Tulyakov, Sergey Abdal, Rameen Yeh, Raymond A. Wonka, Peter Wang, Chaoyang |
| author_facet | Yenphraphai, Jiraphon Chen, Jianqi Wang, Jian Qian, Gordon Tulyakov, Sergey Abdal, Rameen Yeh, Raymond A. Wonka, Peter Wang, Chaoyang |
| contents | Current video-to-4D methods struggle with complex topology changes, transparent materials, thin structures, and inner surfaces. We present Helix4D, a dynamic mesh generation framework by inheriting the expressive representation of Trellis2, adapting it from image-to-3D to video-conditioned 4D generation. Our design arises from two key questions: (a) how to enable Trellis2's frame-local attention to share information across frames while preserving its pretrained quality on rare cases such as transparent objects and inner surfaces, and (b) how to inject temporal information into a purely 3D positional encoding without breaking pretrained capabilities. We address (a) with a sliding-window cross-frame attention and anchor on the first frame. The first frame is generated by the base Trellis2 model and injected into our model, letting it inherit Trellis2's quality in rare cases through cross-frame attention. We address (b) with a 4D temporal encoding that repurposes redundant low-frequency spatial RoPE bands for time, extending the encoding from 3D with no additional parameters. Extensive experiments show the effectiveness of Helix4D for high-quality dynamic mesh generation on ActionBench and our own challenging complex dynamics set. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_26109 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Helix4D: Complex 4D Mesh Generation Yenphraphai, Jiraphon Chen, Jianqi Wang, Jian Qian, Gordon Tulyakov, Sergey Abdal, Rameen Yeh, Raymond A. Wonka, Peter Wang, Chaoyang Computer Vision and Pattern Recognition Current video-to-4D methods struggle with complex topology changes, transparent materials, thin structures, and inner surfaces. We present Helix4D, a dynamic mesh generation framework by inheriting the expressive representation of Trellis2, adapting it from image-to-3D to video-conditioned 4D generation. Our design arises from two key questions: (a) how to enable Trellis2's frame-local attention to share information across frames while preserving its pretrained quality on rare cases such as transparent objects and inner surfaces, and (b) how to inject temporal information into a purely 3D positional encoding without breaking pretrained capabilities. We address (a) with a sliding-window cross-frame attention and anchor on the first frame. The first frame is generated by the base Trellis2 model and injected into our model, letting it inherit Trellis2's quality in rare cases through cross-frame attention. We address (b) with a 4D temporal encoding that repurposes redundant low-frequency spatial RoPE bands for time, extending the encoding from 3D with no additional parameters. Extensive experiments show the effectiveness of Helix4D for high-quality dynamic mesh generation on ActionBench and our own challenging complex dynamics set. |
| title | Helix4D: Complex 4D Mesh Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.26109 |