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Found 35 papers, 15 papers with code
Learning Progressive Joint Propagation for Human Motion Prediction
Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.
3iGS: Factorised Tensorial Illumination for 3D Gaussian Splatting
Instead of optimising a single outgoing radiance parameter, 3iGS enhances 3DGS view-dependent effects by expressing the outgoing radiance as a function of a local illumination field and Bidirectional Reflectance Distribution Function (BRDF) features.
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 introduce MVSplat, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians.
Ranked #1 on
Generalizable Novel View Synthesis
on ACID
Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping
Consequently, we introduce Trajectory Consistency Distillation (TCD), which encompasses trajectory consistency function and strategic stochastic sampling.
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.
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.
ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field
Our method, which we call ABLE-NeRF, significantly reduces `blurry' glossy surfaces in rendering and produces realistic translucent surfaces which lack in prior art.
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.
Entry-Flipped Transformer for Inference and Prediction of Participant Behavior
Our key idea is to model the spatio-temporal relations among participants in a manner that is robust to error accumulation during frame-wise inference and prediction.
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
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation
We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context, which compensates for the deficiency of the classical autoregressive model along pixel space.
Towards Unbiased Visual Emotion Recognition via Causal Intervention
In this work, we scrutinize this problem from the perspective of causal inference, where such dataset characteristic is termed as a confounder which misleads the system to learn the spurious correlation.
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.
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
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.
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.
GeoConv: Geodesic Guided Convolution for Facial Action Unit Recognition
Automatic facial action unit (AU) recognition has attracted great attention but still remains a challenging task, as subtle changes of local facial muscles are difficult to thoroughly capture.
Recovering Facial Reflectance and Geometry from Multi-view Images
While the problem of estimating shapes and diffuse reflectances of human faces from images has been extensively studied, there is relatively less work done on recovering the specular albedo.
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations.
Unconstrained Facial Action Unit Detection via Latent Feature Domain
Due to the combination of source AU-related information and target AU-free information, the latent feature domain with transferred source label can be learned by maximizing the target-domain AU detection performance.
Progress Regression RNN for Online Spatial-Temporal Action Localization in Unconstrained Videos
Previous spatial-temporal action localization methods commonly follow the pipeline of object detection to estimate bounding boxes and labels of actions.
Real-time 3D Face-Eye Performance Capture of a Person Wearing VR Headset
Teleconference or telepresence based on virtual reality (VR) headmount display (HMD) device is a very interesting and promising application since HMD can provide immersive feelings for users.
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
Conditional Adversarial Synthesis of 3D Facial Action Units
Employing deep learning-based approaches for fine-grained facial expression analysis, such as those involving the estimation of Action Unit (AU) intensities, is difficult due to the lack of a large-scale dataset of real faces with sufficiently diverse AU labels for training.
A Generative Model for Depth-Based Robust 3D Facial Pose Tracking
We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations.
Modality and Component Aware Feature Fusion For RGB-D Scene Classification
While convolutional neural networks (CNN) have been excellent for object recognition, the greater spatial variability in scene images typically meant that the standard full-image CNN features are suboptimal for scene classification.
MMSS: Multi-Modal Sharable and Specific Feature Learning for RGB-D Object Recognition
We first construct deep CNN layers for color and depth separately, and then connect them with our carefully designed multi-modal layers, which fuse color and depth information by enforcing a common part to be shared by features of different modalities.
Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes
We introduce a novel robust hybrid 3D face tracking framework from RGBD video streams, which is capable of tracking head pose and facial actions without pre-calibration or intervention from a user.
Recovering Surface Details under General Unknown Illumination Using Shading and Coarse Multi-view Stereo
As a result, our approach is robust, stable and is able to efficiently recover high quality of surface details even starting with a coarse MVS.
