MagicPose4D: Crafting Articulated Models with Appearance and Motion Control
<|reference_start|>MagicPose4D: Crafting Articulated Models with Appearance and Motion Control: With the success of 2D and 3D visual generative models, there is growing interest in generating 4D content. Existing methods primarily rely on text prompts to produce 4D content, but they often fall short of accurately defining complex or rare motions. To address this limitation, we propose MagicPose4D, a novel framework for refined control over both appearance and motion in 4D generation. Unlike traditional methods, MagicPose4D accepts monocular videos as motion prompts, enabling precise and customizable motion generation. MagicPose4D comprises two key modules:   i) Dual-Phase 4D Reconstruction Module} which operates in two phases. The first phase focuses on capturing the model's shape using accurate 2D supervision and less accurate but geometrically informative 3D pseudo-supervision without imposing skeleton constraints. The second phase refines the model using more accurate pseudo-3D supervision, obtained in the first phase and introduces kinematic chain-based skeleton constraints to ensure physical plausibility. Additionally, we propose a Global-local Chamfer loss that aligns the overall distribution of predicted mesh vertices with the supervision while maintaining part-level alignment without extra annotations.   ii) Cross-category Motion Transfer Module} leverages the predictions from the 4D reconstruction module and uses a kinematic-chain-based skeleton to achieve cross-category motion transfer. It ensures smooth transitions between frames through dynamic rigidity, facilitating robust generalization without additional training.   Through extensive experiments, we demonstrate that MagicPose4D significantly improves the accuracy and consistency of 4D content generation, outperforming existing methods in various benchmarks.<|reference_end|>
@article{zhang2024magicpose4d:,
    title={MagicPose4D: Crafting Articulated Models with Appearance and Motion
  Control},
    author={Hao Zhang, Di Chang, Fang Li, Mohammad Soleymani, Narendra Ahuja},
    journal={arXiv preprint arXiv:2405.14017},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14017},
    primaryClass={cs.CV}
}