Search Results for author:
Found 23 papers, 8 papers with code
Gaga: Group Any Gaussians via 3D-aware Memory Bank
We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot segmentation models.
GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations.
A Unified Approach for Text- and Image-guided 4D Scene Generation
Large-scale diffusion generative models are greatly simplifying image, video and 3D asset creation from user-provided text prompts and images.
Pyramid Diffusion for Fine 3D Large Scene Generation
Directly transferring the 2D techniques to 3D scene generation is challenging due to significant resolution reduction and the scarcity of comprehensive real-world 3D scene datasets.
3D Reconstruction with Generalizable Neural Fields using Scene Priors
NFP not only demonstrates SOTA scene reconstruction performance and efficiency, but it also supports single-image novel-view synthesis, which is underexplored in neural fields.
Emotional Intelligence of Large Language Models
Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI, suitable for both humans and LLMs.
Generalizable One-shot Neural Head Avatar
We present a method that reconstructs and animates a 3D head avatar from a single-view portrait image.
Zero-shot Pose Transfer for Unrigged Stylized 3D Characters
We present a zero-shot approach that requires only the widely available deformed non-stylized avatars in training, and deforms stylized characters of significantly different shapes at inference.
TUVF: Learning Generalizable Texture UV Radiance Fields
This allows the texture to be disentangled from the underlying shape and transferable to other shapes that share the same UV space, i. e., from the same category.
Affordance Diffusion: Synthesizing Hand-Object Interactions
In contrast, in this work we focus on synthesizing complex interactions (ie, an articulated hand) with a given object.
Self-Supervised Super-Plane for Neural 3D Reconstruction
In this work, we introduce a self-supervised super-plane constraint by exploring the free geometry cues from the predicted surface, which can further regularize the reconstruction of plane regions without any other ground truth annotations.
Physics-based Indirect Illumination for Inverse Rendering
We present a physics-based inverse rendering method that learns the illumination, geometry, and materials of a scene from posed multi-view RGB images.
Autoregressive 3D Shape Generation via Canonical Mapping
With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation.
Learning Continuous Environment Fields via Implicit Functions
We propose a novel scene representation that encodes reaching distance -- the distance between any position in the scene to a goal along a feasible trajectory.
Learning 3D Dense Correspondence via Canonical Point Autoencoder
We propose a canonical point autoencoder (CPAE) that predicts dense correspondences between 3D shapes of the same category.
Online Adaptation for Consistent Mesh Reconstruction in the Wild
This paper presents an algorithm to reconstruct temporally consistent 3D meshes of deformable object instances from videos in the wild.
Self-supervised Single-view 3D Reconstruction via Semantic Consistency
To the best of our knowledge, we are the first to try and solve the single-view reconstruction problem without a category-specific template mesh or semantic keypoints.
Joint-task Self-supervised Learning for Temporal Correspondence
Our learning process integrates two highly related tasks: tracking large image regions \emph{and} establishing fine-grained pixel-level associations between consecutive video frames.
Learning Propagation for Arbitrarily-structured Data
We experiment with semantic segmentation networks, where we use our propagation module to jointly train on different data -- images, superpixels and point clouds.
Learning Linear Transformations for Fast Image and Video Style Transfer
Given a random pair of images, a universal style transfer method extracts the feel from a reference image to synthesize an output based on the look of a content image.
Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
In order to predict valid affordances and learn possible 3D human poses in indoor scenes, we need to understand the semantic and geometric structure of a scene as well as its potential interactions with a human.
Learning Linear Transformations for Fast Arbitrary Style Transfer
Recent arbitrary style transfer methods transfer second order statistics from reference image onto content image via a multiplication between content image features and a transformation matrix, which is computed from features with a pre-determined algorithm.
A Closed-form Solution to Photorealistic Image Stylization
Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic.
