1.3k citations · 2.6k across the 63 of their papers we have counts for
12 papers · 1 filter
MVDD: Multi-View Depth Diffusion Models
Zhen Wang, Qiangeng Xu, Feitong Tan +6
Denoising diffusion models have demonstrated outstanding results in 2D image generation, yet it remains a challenge to replicate its success in 3D shape generation. In this paper,…
Gaussian3Diff: 3D Gaussian Diffusion for 3D Full Head Synthesis and Editing
Yushi Lan, Feitong Tan, Di Qiu +8
We present a novel framework for generating photorealistic 3D human head and subsequently manipulating and reposing them with remarkable flexibility. The proposed approach leverage…
InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs
Zhongyi Zhou, Jing Jin, Vrushank Phadnis +16
Visual programming has the potential of providing novice programmers with a low-code experience to build customized processing pipelines. Existing systems typically require users t…
Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor
Xinyang Liu, Yijin Li, Yanbin Teng +4
Light-weight time-of-flight (ToF) depth sensors are compact and cost-efficient, and thus widely used on mobile devices for tasks such as autofocus and obstacle detection. However,…
Novel-view Synthesis and Pose Estimation for Hand-Object Interaction from Sparse Views
Wentian Qu, Zhaopeng Cui, Yinda Zhang +4
Hand-object interaction understanding and the barely addressed novel view synthesis are highly desired in the immersive communication, whereas it is challenging due to the high def…
Spectral Graphormer: Spectral Graph-based Transformer for Egocentric Two-Hand Reconstruction using Multi-View Color Images
Tze Ho Elden Tse, Franziska Mueller, Zhengyang Shen +8
We propose a novel transformer-based framework that reconstructs two high fidelity hands from multi-view RGB images. Unlike existing hand pose estimation methods, where one typical…
Self-supervised Learning of Implicit Shape Representation with Dense Correspondence for Deformable Objects
Baowen Zhang, Jiahe Li, Xiaoming Deng +3
Learning 3D shape representation with dense correspondence for deformable objects is a fundamental problem in computer vision. Existing approaches often need additional annotations…
Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions
Yun He, Danhang Tang, Yinda Zhang +2
Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3D coordinate prediction. However,they usually suffer from two crit…
Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos
Ziqian Bai, Feitong Tan, Zeng Huang +12
We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achie…
Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur
Peng Dai, Yinda Zhang, Xin Yu +2
Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of la…
Learning Versatile 3D Shape Generation with Improved AR Models
Simian Luo, Xuelin Qian, Yanwei Fu +5
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space. While this approach has been extended to the…
SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field
Chong Bao, Yinda Zhang, Bangbang Yang +5
Despite the great success in 2D editing using user-friendly tools, such as Photoshop, semantic strokes, or even text prompts, similar capabilities in 3D areas are still limited, ei…