SWiT-4D: Sliding-Window Transformer for Lossless and Parameter-Free Temporal 4D Generation

• URL: http://arxiv.org/abs/2512.10860v1
• Date: Thu, 11 Dec 2025 17:54:31 GMT
• Title: SWiT-4D: Sliding-Window Transformer for Lossless and Parameter-Free Temporal 4D Generation
• Authors: Kehong Gong, Zhengyu Wen, Mingxi Xu, Weixia He, Qi Wang, Ning Zhang, Zhengyu Li, Chenbin Li, Dongze Lian, Wei Zhao, Xiaoyu He, Mingyuan Zhang,
• Abstract summary: SWiT-4D is a Sliding-Window Transformer for lossless, parameter-free temporal 4D mesh generation.<n> SWiT-4D integrates seamlessly with any Diffusion Transformer (DiT)-based image-to-3D generator.<n>It achieves high-fidelity geometry and stable temporal consistency, indicating practical deployability under extremely limited 4D supervision.
• Score: 30.72482055095692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite significant progress in 4D content generation, the conversion of monocular videos into high-quality animated 3D assets with explicit 4D meshes remains considerably challenging. The scarcity of large-scale, naturally captured 4D mesh datasets further limits the ability to train generalizable video-to-4D models from scratch in a purely data-driven manner. Meanwhile, advances in image-to-3D generation, supported by extensive datasets, offer powerful prior models that can be leveraged. To better utilize these priors while minimizing reliance on 4D supervision, we introduce SWiT-4D, a Sliding-Window Transformer for lossless, parameter-free temporal 4D mesh generation. SWiT-4D integrates seamlessly with any Diffusion Transformer (DiT)-based image-to-3D generator, adding spatial-temporal modeling across video frames while preserving the original single-image forward process, enabling 4D mesh reconstruction from videos of arbitrary length. To recover global translation, we further introduce an optimization-based trajectory module tailored for static-camera monocular videos. SWiT-4D demonstrates strong data efficiency: with only a single short (<10s) video for fine-tuning, it achieves high-fidelity geometry and stable temporal consistency, indicating practical deployability under extremely limited 4D supervision. Comprehensive experiments on both in-domain zoo-test sets and challenging out-of-domain benchmarks (C4D, Objaverse, and in-the-wild videos) show that SWiT-4D consistently outperforms existing baselines in temporal smoothness. Project page: https://animotionlab.github.io/SWIT4D/

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