Search Results for author:
Found 25 papers, 16 papers with code
DragAPart: Learning a Part-Level Motion Prior for Articulated Objects
We introduce DragAPart, a method that, given an image and a set of drags as input, can generate a new image of the same object in a new state, compatible with the action of the drags.
ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition
3D decomposition/segmentation still remains a challenge as large-scale 3D annotated data is not readily available.
MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
We propose MVSplat, an efficient feed-forward 3D Gaussian Splatting model learned from sparse multi-view images.
Ranked #1 on
Generalizable Novel View Synthesis
on ACID
Amodal Ground Truth and Completion in the Wild
In contrast, we use 3D data to establish an automatic pipeline to determine authentic ground truth amodal masks for partially occluded objects in real images.
Free3D: Consistent Novel View Synthesis without 3D Representation
Similar to Zero-1-to-3, we start from a pre-trained 2D image generator for generalization, and fine-tune it for NVS.
One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls
By integrating a compact network and incorporating an additional simple yet effective step during inference, OMS elevates image fidelity and harmonizes the dichotomy between training and inference, while preserving original model parameters.
What Does Stable Diffusion Know about the 3D Scene?
(iii) We find that features from Stable Diffusion are good for discriminative learning of a number of properties, including scene geometry, support relations, shadows and depth, but less performant for occlusion and material.
Online Clustered Codebook
Vector Quantisation (VQ) is experiencing a comeback in machine learning, where it is increasingly used in representation learning.
PanoDiffusion: 360-degree Panorama Outpainting via Diffusion
Generating complete 360-degree panoramas from narrow field of view images is ongoing research as omnidirectional RGB data is not readily available.
Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation
In this work, we propose Cocktail, a pipeline to mix various modalities into one embedding, amalgamated with a generalized ControlNet (gControlNet), a controllable normalisation (ControlNorm), and a spatial guidance sampling method, to actualize multi-modal and spatially-refined control for text-conditional diffusion models.
Explicit Correspondence Matching for Generalizable Neural Radiance Fields
The key to our approach lies in the explicitly modeled correspondence matching information, so as to provide the geometry prior to the prediction of NeRF color and density for volume rendering.
Vector Quantized Wasserstein Auto-Encoder
Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks.
Unified Discrete Diffusion for Simultaneous Vision-Language Generation
The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals.
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar patches within an image into the same index, resulting in a repeated artifact for similar adjacent regions using existing decoder architectures.
Object-Compositional Neural Implicit Surfaces
This paper proposes a novel framework, ObjectSDF, to build an object-compositional neural implicit representation with high fidelity in 3D reconstruction and object representation.
High-Quality Pluralistic Image Completion via Code Shared VQGAN
In this work, we present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed.
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
In light of recent advances in NeRF-based 3D-aware generative models, we introduce a new task, Semantic-to-NeRF translation, that aims to reconstruct a 3D scene modelled by NeRF, conditioned on one single-view semantic mask as input.
Ranked #1 on
3D-Aware Image Synthesis
on CelebAMask-HQ
Synthesizing Photorealistic Images with Deep Generative Learning
The goal of this thesis is to present my research contributions towards solving various visual synthesis and generation tasks, comprising image translation, image completion, and completed scene decomposition.
AgileGAN: stylizing portraits by inversion-consistent transfer learning
While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real input images.
Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition
In this work, we propose a higher-level scene understanding system to tackle both visible and invisible parts of objects and backgrounds in a given scene.
The Spatially-Correlative Loss for Various Image Translation Tasks
We propose a novel spatially-correlative loss that is simple, efficient and yet effective for preserving scene structure consistency while supporting large appearance changes during unpaired image-to-image (I2I) translation.
Bridging Global Context Interactions for High-Fidelity Image Completion
Bridging global context interactions correctly is important for high-fidelity image completion with large masks.
Ranked #2 on
Image Inpainting
on FFHQ 512 x 512
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder
Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.
Pluralistic Image Completion
In this paper, we present an approach for \textbf{pluralistic image completion} -- the task of generating multiple and diverse plausible solutions for image completion.
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks
Current methods for single-image depth estimation use training datasets with real image-depth pairs or stereo pairs, which are not easy to acquire.
Ranked #3 on
Depth Estimation
on eBDtheque
