The development of multi-view image synthesis is constrained by the scarcity of training data. One promising solution is to finetune well-trained video generative models to synthesize 360-degree videos of objects. While these methods benefit from the strong generative priors inherited from the pretrained knowledge, they are limited by the high computational costs incurred by the large number of viewpoints. Existing methods commonly adopt temporal attention mechanism to address this. However, these methods suffer from undesirable artifacts such as 3D inconsistency and over-smoothing in the generated results. In this paper, we introduce a novel approach to unlock the video priors for multi-view synthesis by reducing generation into a sparser yet more precise process. Specifically, we introduce two strategies to achieve this: i) Condensing the video diffusion model to synthesize highly consistent sparse multiview images. ii) Extracting dense geometrical priors from the pretrained video diffusion models to enhance the generation stability. The combination of these two strategies formulates a novel framework for multi-view synthesis, which is capable of synthesizing highly consistent sparse multiview images with strong generalization ability. Extensive experiments demonstrate that our approach achieves superior efficiency, generalization, and consistency, outperforming state-of-the-art multi-view synthesis methods.

The rapid advancement of 3D Generative Adversarial Networks (GANs) has significantly enhanced the diversity and quality of generated 3D images. Despite these breakthroughs, the manipulation capabilities of 3D GANs remain unexplored, presenting substantial challenges for practical applications where user interaction and modification are essential. Current manipulation methods often lack the precision needed for fine-grained attribute manipulation, and struggle to maintain multi-view consistency during the editing process. To address these limitations, we propose 3DHumanEdit, a novel approach for 3D human body part-aware manipulation. 3DHumanEdit leverages multi-modal feature fusion and body part-aware feature alignment to achieve precise manipulation of individual body parts based on detailed text inputs and segmentation images. By exploring 3D prior for accurate editing and enforcing correspondence in latent space, 3DHumanEdit ensures coherence across multiple views. Experiments demonstrate that 3DHumanEdit outperforms existing methods in both editing fidelity and multi-view consistency, offering a robust solution for fine-grained 3D manipulation.

With the explosive growth of 3D content creation, there is an increasing demand for automatically converting static 3D models into articulation-ready versions that support realistic animation. Traditional approaches rely heavily on manual annotation, which is both time-consuming and labor-intensive. Moreover, the lack of large-scale benchmarks has hindered the development of learning-based solutions. In this work, we present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets. Our key contributions are threefold. First, we introduce Articulation-XL, a large-scale benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from Objaverse-XL. Second, we propose a novel skeleton generation method that formulates the task as a sequence modeling problem, leveraging an autoregressive transformer to naturally handle varying numbers of bones or joints within skeletons and their inherent dependencies across different 3D models. Third, we predict skinning weights using a functional diffusion process that incorporates volumetric geodesic distance priors between vertices and joints. Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories, achieving high-quality articulation that enables realistic animation. Project page: https://chaoyuesong.github.io/MagicArticulate.

Modern interactive applications increasingly demand dynamic 3D content, yet the transformation of static 3D models into animated assets constitutes a significant bottleneck in content creation pipelines. While recent advances in generative AI have revolutionized static 3D model creation, rigging and animation continue to depend heavily on expert intervention. We present \textbf{Puppeteer}, a comprehensive framework that addresses both automatic rigging and animation for diverse 3D objects. Our system first predicts plausible skeletal structures via an auto-regressive transformer that introduces a joint-based tokenization strategy for compact representation and a hierarchical ordering methodology with stochastic perturbation that enhances bidirectional learning capabilities. It then infers skinning weights via an attention-based architecture incorporating topology-aware joint attention that explicitly encodes inter-joint relationships based on skeletal graph distances. Finally, we complement these rigging advances with a differentiable optimization-based animation pipeline that generates stable, high-fidelity animations while being computationally more efficient than existing approaches. Extensive evaluations across multiple benchmarks demonstrate that our method significantly outperforms state-of-the-art techniques in both skeletal prediction accuracy and skinning quality. The system robustly processes diverse 3D content, ranging from professionally designed game assets to AI-generated shapes, producing temporally coherent animations that eliminate the jittering issues common in existing methods.

Training a generative model with limited data (e.g., 10) is a very challenging task. Many works propose to fine-tune a pretrained GAN model. However, this can easily result in overfitting. In other words, they manage to adapt the style but fail to preserve the content, where style denotes the specific properties that define a domain while content denotes the domain-irrelevant information that represents diversity. Recent works try to maintain a predefined correspondence to preserve the content, however, the diversity is still not enough and it may affect style adaptation. In this work, we propose a paired image reconstruction approach for content preservation. We propose to introduce an image translation module to GAN transferring, where the module teaches the generator to separate style and content, and the generator provides training data to the translation module in return. Qualitative and quantitative experiments show that our method consistently surpasses the state-of-the-art methods in a few-shot setting.

Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g., faces on the back view) and inaccurate shapes (e.g., animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure multi-view consistency while struggling to balance 2D generation quality with 3D consistency. In this paper, we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically, we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. Moreover, to ensure accurate appearances of different views, we further modulate the output of the 2D diffusion model to the correct patterns of the template views without altering the generated object's style. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model. Extensive experiments show our method can largely improve the multi-view consistency while retaining fidelity and diversity. Our project page is available at: https://stellarcheng.github.io/Sculpt3D/.

Editable 3D-aware generation, which supports user-interacted editing, has witnessed rapid development re-cently. However, existing editable 3D GANs either fail to achieve high-accuracy local editing or suffer from huge computational costs. We propose AttriHuman-3D, an ed-itable 3D human generation model, which address the aforementioned problems with attribute decomposition and indexing. The core idea of the proposed model is to generate all attributes (e.g. human body, hair, clothes and so on) in an overall attribute space with six feature planes, which are then decomposed and manipulated with different attribute indexes. To precisely extract features of different attributes from the generated feature planes, we propose a novel at-tribute indexing method as well as an orthogonal projection regularization to enhance the disentanglement. We also introduce a hyper-latent training strategy and an attribute-specific sampling strategy to avoid style entanglement and misleading punishment from the discriminator. Our method allows users to interactively edit selected attributes in the generated 3D human avatars while keeping others fixed. Both qualitative and quantitative experiments demonstrate that our model provides a strong disentanglement between different attributes, allows fine-grained image editing and generates high-quality 3D human avatars.

Garment transfer shows great potential in realistic applications with the goal of transfering outfits across different people images. However, garment transfer between images with heavy misalignments or severe occlusions still remains as a challenge. In this work, we propose Complementary Transfering Network (CT-Net) to adaptively model different levels of geometric changes and transfer outfits between different people. In specific, CT-Net consists of three modules: i) A complementary warping module first estimates two complementary warpings to transfer the desired clothes in different granularities. ii) A layout prediction module is proposed to predict the target layout, which guides the preservation or generation of the body parts in the synthesized images. iii) A dynamic fusion module adaptively combines the advantages of the complementary warpings to render the garment transfer results. Extensive experiments conducted on DeepFashion dataset demonstrate that our network synthesizes high-quality garment transfer images and significantly outperforms the state-of-art methods both qualitatively and quantitatively. Our source code will be available online.