We introduce EnerVerse, a generative robotics foundation model that constructs and interprets embodied spaces. EnerVerse employs a chunk-wise autoregressive video diffusion framework to predict future embodied spaces from instructions, enhanced by a sparse context memory for long-term reasoning. To model the 3D robotics world, we adopt a multi-view video representation, providing rich perspectives to address challenges like motion ambiguity and 3D grounding. Additionally, EnerVerse-D, a data engine pipeline combining generative modeling with 4D Gaussian Splatting, forms a self-reinforcing data loop to reduce the sim-to-real gap. Leveraging these innovations, EnerVerse translates 4D world representations into physical actions via a policy head (EnerVerse-A), achieving state-of-the-art performance in both simulation and real-world tasks. For efficiency, EnerVerse-A reuses features from the first denoising step and predicts action chunks, achieving about 280 ms per 8-step action chunk on a single RTX 4090. Further video demos, dataset samples could be found in our project page.

We propose CrossHuman, a novel method that learns cross-guidance from parametric human model and multi-frame RGB images to achieve high-quality 3D human reconstruction. To recover geometry details and texture even in invisible regions, we design a reconstruction pipeline combined with tracking-based methods and tracking-free methods. Given a monocular RGB sequence, we track the parametric human model in the whole sequence, the points (voxels) corresponding to the target frame are warped to reference frames by the parametric body motion. Guided by the geometry priors of the parametric body and spatially aligned features from RGB sequence, the robust implicit surface is fused. Moreover, a multi-frame transformer (MFT) and a self-supervised warp refinement module are integrated to the framework to relax the requirements of parametric body and help to deal with very loose cloth. Compared with previous works, our CrossHuman enables high-fidelity geometry details and texture in both visible and invisible regions and improves the accuracy of the human reconstruction even under estimated inaccurate parametric human models. The experiments demonstrate that our method achieves state-of-the-art (SOTA) performance.

We propose FloRen, a novel system for real-time, high-resolution free-view human synthesis. Our system runs at 15fps in 1K resolution with very sparse RGB cameras. In FloRen, a coarse-level implicit geometry is recovered at first as initialization, and then processed by a neural rendering framework based on appearance flow. Our appearance flow-based rendering framework consists of three steps, namely view-dependent depth refinement, appearance flow estimation and occlusion-aware color rendering. In this way, we resolve the view synthesis problem in the image plane, where 2D convolutional neural networks can be efficiently applied, contributing to high speed performance. For robust appearance flow estimation, we explicitly combine data-driven human prior knowledge with multiview geometric constraints. The accurate appearance flow enables precise color mapping from input view to novel view, which greatly facilitates high-resolution novel view generation. We demonstrate that our system achieves state-of-the-art performance and even outperforms many offline methods.