ES3D: Embedding Semantics into 3D Space for Component-Aware Editing

Existing 3D editing methods have made notable progress in controllability, yet they remain limited in several important ways. Most approaches rely on text-driven editing, which struggles to express fine-grained visual changes intended by the user. Moreover, many methods require manually supplied 3D masks or introduce unintended changes to regions that should remain untouched. These limitations largely arise from the absence of fine-grained semantic understanding, making it difficult for existing models to retrieve or modify specific 3D components. We introduce ES3D, a framework that embeds semantics directly into 3D space, enabling component-aware retrieval and editing of a 3D asset conditioned on multiple local reference images and optional text queries. We first construct a 3D semantic embedding by projecting multi-view semantic features into the voxelized space of the asset. We then perform 3D component retrieval by computing feature similarity between the 3D semantic embedding and the semantic embeddings of image or text queries. For editing, we employ a pretrained 3D generative model with an inpainting mechanism to modify the retrieved components guided by user-provided images while preserving the rest of the asset. Overall, ES3D is a 3D editing framework that retrieves editable regions based on semantic cues and uses multiple images as conditions. Extensive experiments demonstrate that ES3D produces geometrically consistent and semantically coherent edits, enabling robust image-based and text-assisted control for 3D editing.

GeRM: A Generative Rendering Model From Physically Realistic to Photorealistic

For decades, Physically-Based Rendering (PBR) is the fundation of synthesizing photorealisitic images, and therefore sometimes roughly referred as Photorealistic Rendering (PRR). While PBR is indeed a mathematical simulation of light transport that guarantees physical reality, photorealism has additional reliance on the realistic digital model of geometry and appearance of the real world, leaving a barely explored gap from PBR to PRR (P2P). Consequently, the path toward photorealism faces a critical dilemma: the explicit simulation of PRR encumbered by unreachable realistic digital models for real-world existence, while implicit generation models sacrifice controllability and geometric consistency. Based on this insight, this paper presents the problem, data, and approach of mitigating P2P gap, followed by the first multi-modal generative rendering model, dubbed GeRM, to unify PBR and PRR. GeRM integrates physical attributes like G-buffers with text prompts, and progressive incremental injection to generate controllable photorealistic images, allowing users to fluidly navigate the continuum between strict physical fidelity and perceptual photorealism. Technically, we model the transition between PBR and PRR images as a distribution transfer and aim to learn a distribution transfer vector field (DTV Field) to guide this process. To define the learning objective, we first leverage a multi-agent VLM framework to construct an expert-guided pairwise P2P transfer dataset, named P2P-50K, where each paired sample in the dataset corresponds to a transfer vector in the DTV Field. Subsequently, we propose a multi-condition ControlNet to learn the DTV Field, which synthesizes PBR images and progressively transitions them into PRR images, guided by G-buffers, text prompts, and cues for enhanced regions.

PFAvatar: Pose-Fusion 3D Personalized Avatar Reconstruction from Real-World Outfit-of-the-Day Photos

We propose PFAvatar (Pose-Fusion Avatar), a new method that reconstructs high-quality 3D avatars from Outfit of the Day(OOTD) photos, which exhibit diverse poses, occlusions, and complex backgrounds. Our method consists of two stages: (1) fine-tuning a pose-aware diffusion model from few-shot OOTD examples and (2) distilling a 3D avatar represented by a neural radiance field (NeRF). In the first stage, unlike previous methods that segment images into assets (e.g., garments, accessories) for 3D assembly, which is prone to inconsistency, we avoid decomposition and directly model the full-body appearance. By integrating a pre-trained ControlNet for pose estimation and a novel Condition Prior Preservation Loss (CPPL), our method enables end-to-end learning of fine details while mitigating language drift in few-shot training. Our method completes personalization in just 5 minutes, achieving a 48x speed-up compared to previous approaches. In the second stage, we introduce a NeRF-based avatar representation optimized by canonical SMPL-X space sampling and Multi-Resolution 3D-SDS. Compared to mesh-based representations that suffer from resolution-dependent discretization and erroneous occluded geometry, our continuous radiance field can preserve high-frequency textures (e.g., hair) and handle occlusions correctly through transmittance. Experiments demonstrate that PFAvatar outperforms state-of-the-art methods in terms of reconstruction fidelity, detail preservation, and robustness to occlusions/truncations, advancing practical 3D avatar generation from real-world OOTD albums. In addition, the reconstructed 3D avatar supports downstream applications such as virtual try-on, animation, and human video reenactment, further demonstrating the versatility and practical value of our approach.