Hongyu Liu

We introduce AvatarPointillist, a novel framework for generating dynamic 4D Gaussian avatars from a single portrait image. At the core of our method is a decoder-only Transformer that autoregressively generates a point cloud for 3D Gaussian Splatting. This sequential approach allows for precise, adaptive construction, dynamically adjusting point density and the total number of points based on the subject’s complexity. During point generation, the AR model also jointly predicts per-point binding information, enabling realistic animation. After generation, a dedicated Gaussian decoder converts the points into complete, renderable Gaussian attributes. We demonstrate that conditioning the decoder on the latent features from the AR generator enables effective interaction between stages and markedly improves fidelity. Extensive experiments validate that AvatarPointillist produces high-quality, photorealistic, and controllable avatars. We believe this autoregressive formulation represents a new paradigm for avatar generation.

We present HeadArtist-VL, a 3D head generation method that supports either vision or language input, utilizing a landmark-guided ControlNet as a generative prior and introducing a self-score distillation (SSD) pipeline that optimizes a parameterized 3D head model under the supervision of the prior itself, where given a sampled camera pose, an image and its landmarks are rendered from the head model, noised, and processed through a frozen ControlNet twice with different classifier-free guidance (CFG) weights to guide rendering alignment with language prompts, or for reference images, an image encoder extracts identity embeddings sent to the ControlNet, while a novel-view diffusion model synthesizes the reference image under the sampled pose to guide SSD, producing high-quality 3D head sculptures with rich geometry, photo-realistic appearance, and support for editing operations, significantly outperforming state-of-the-art methods.

This work focuses on open-domain 4D avatarization, with the purpose of creating a 4D avatar from a portrait image in an arbitrary style. We select parametric triplanes as the intermediate 4D representation and propose a practical training paradigm that takes advantage of both generative adversarial networks (GANs) and diffusion models. Our design stems from the observation that 4D GANs excel at bridging images and triplanes without supervision yet usually face challenges in handling diverse data distributions. A robust 2D diffusion prior emerges as the solution, assisting the GAN in transferring its expertise across various domains. The synergy between these experts permits the construction of a multi-domain image-triplane dataset, which drives the development of a general 4D avatar creator. Extensive experiments suggest that our model, AvatarArtist, is capable of producing high-quality 4D avatars with strong robustness to various source image domains. The code, the data, and the models will be made publicly available to facilitate future studies..