anyeZHY/tesseract
<p align="center"> <h1 align="center">TesserAct: Learning 4D Embodied World Models</h1> <p align="center"> <a href="https://haoyuzhen.com">Haoyu Zhen</a>, <a href="https://qiaosun22.github.io/">Qiao Sun</a>, <a href="https://icefoxzhx.github.io/">Hongxin Zhang</a>, <a href="https://senfu.github.io/">Junyan Li</a>, <a href="https://rainbow979.github.io/">Siyuan Zhou</a>, <a href="https://yilundu.github.io/">Yilun Du</a>, <a href="https://people.csail.mit.edu/ganchuang">Chuang Gan</a> </p> </p>
<p align="center"> <a href="https://arxiv.org/abs/2504.20995">Paper PDF</a> | <a href="https://tesseractworld.github.io">Project Page</a> | <a href="https://huggingface.co/anyeZHY/tesseract">Model on Hugging Face</a> | <a href="https://github.com/UMass-Embodied-AGI/TesserAct">Code</a> </p>
We propose TesserAct, the 4D Embodied World Model, which takes input images and text instruction to generate RGB, depth, and normal videos, reconstructing a 4D scene and predicting actions.