Creating detailed 3D characters from a single image remains challenging due to the difficulty in separating semantic components during generation. Existing methods often produce entangled meshes with poor topology, hindering downstream applications like rigging and animation. We introduce SeparateGen, a novel framework that generates high-quality 3D characters by explicitly reconstructing them as distinct semantic components (e.g., body, clothing, hair, shoes) from a single, arbitrary-pose image. SeparateGen first leverages a multi-view diffusion model to generate consistent multi-view images in a canonical A-pose. Then, a novel component-aware reconstruction model, SC-LRM, conditioned on these multi-view images, adaptively decomposes and reconstructs each component with high fidelity. To train and evaluate SeparateGen, we contribute SC-Anime, the first large-scale dataset of 7,580 anime-style 3D characters with detailed component-level annotations. Extensive experiments demonstrate that SeparateGen significantly outperforms state-of-the-art methods in both reconstruction quality and multi-view consistency. Furthermore, our component-based approach effectively resolves mesh entanglement issues, enabling seamless rigging and asset reuse. SeparateGen thus represents a step towards generating high-quality, application-ready 3D characters from a single image. The SC-Anime dataset and our code will be publicly released.

Neural radiance fields (NeRFs) for novel-view synthesis have attracted the attention of researchers in computer vision and graphics.Unlike traditional methods using explicit expressions, NeRFs represent a scene as an implicit neural radiance field.When rendering, NeRF queries the color density at every position in the scene through a neural network.NeRF brings a wide range of possibilities for real-world 3D reconstruction and rendering, but problems remain to be solved.Previous works have improved NeRF's sampling technique, position encoding method, network structure, etc., but these improvements are difficult to be combined as the different modules are not well decoupled.Recent works have significantly sped up the core GPU computation of NeRF, leaving the deep learning framework as a major computational cost.Thus, it has been suggested to replace the frameworks by pure CUDA programs, but this limits maintainability and extendability.Therefore, we propose JNeRF, a unified, efficient, framework-friendly NeRF model zoo based on Jittor.