Search Results for author:
Found 22 papers, 7 papers with code
VR-NeRF: High-Fidelity Virtualized Walkable Spaces
We present an end-to-end system for the high-fidelity capture, model reconstruction, and real-time rendering of walkable spaces in virtual reality using neural radiance fields.
EgoHumans: An Egocentric 3D Multi-Human Benchmark
We present EgoHumans, a new multi-view multi-human video benchmark to advance the state-of-the-art of egocentric human 3D pose estimation and tracking.
Ego-Humans: An Ego-Centric 3D Multi-Human Benchmark
We present EgoHumans, a new multi-view multi-human video benchmark to advance the state-of-the-art of egocentric human 3D pose estimation and tracking.
Neural Pixel Composition for 3D-4D View Synthesis From Multi-Views
We present Neural Pixel Composition (NPC), a novel approach for continuous 3D-4D view synthesis given only a discrete set of multi-view observations as input.
Neural Pixel Composition: 3D-4D View Synthesis from Multi-Views
We present Neural Pixel Composition (NPC), a novel approach for continuous 3D-4D view synthesis given only a discrete set of multi-view observations as input.
KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints
In this work, we investigate common issues with existing spatial encodings and propose a simple yet highly effective approach to modeling high-fidelity volumetric humans from sparse views.
Ranked #2 on
Generalizable Novel View Synthesis
on ZJU-MoCap
COAP: Compositional Articulated Occupancy of People
We present a novel neural implicit representation for articulated human bodies.
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes
Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction.
Streaming Self-Training via Domain-Agnostic Unlabeled Images
We present streaming self-training (SST) that aims to democratize the process of learning visual recognition models such that a non-expert user can define a new task depending on their needs via a few labeled examples and minimal domain knowledge.
Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos
Associating latent codes of a video and manifold projection enables users to make desired edits.
Improving task-specific representation via 1M unlabelled images without any extra knowledge
We improve surface normal estimation on NYU-v2 depth dataset and semantic segmentation on PASCAL VOC by 4% over base model.
4D Visualization of Dynamic Events from Unconstrained Multi-View Videos
We present a data-driven approach for 4D space-time visualization of dynamic events from videos captured by hand-held multiple cameras.
Unsupervised Audiovisual Synthesis via Exemplar Autoencoders
We present an unsupervised approach that converts the input speech of any individual into audiovisual streams of potentially-infinitely many output speakers.
Shapes and Context: In-the-Wild Image Synthesis & Manipulation
We introduce a data-driven approach for interactively synthesizing in-the-wild images from semantic label maps.
Recycle-GAN: Unsupervised Video Retargeting
We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i. e., if contents of John Oliver's speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert's style.
Patch Correspondences for Interpreting Pixel-level CNNs
We present compositional nearest neighbors (CompNN), a simple approach to visually interpreting distributed representations learned by a convolutional neural network (CNN) for pixel-level tasks (e. g., image synthesis and segmentation).
PixelNN: Example-based Image Synthesis
We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an "incomplete" signal such as a low-resolution image, a surface normal map, or edges.
Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting
To this end, we demonstrate faster convergence and better performance on diverse classification tasks: image classification using CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012.
PixelNet: Representation of the pixels, by the pixels, and for the pixels
We explore design principles for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation.
PixelNet: Towards a General Pixel-level Architecture
We explore architectures for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation.
Marr Revisited: 2D-3D Alignment via Surface Normal Prediction
We introduce an approach that leverages surface normal predictions, along with appearance cues, to retrieve 3D models for objects depicted in 2D still images from a large CAD object library.
Mid-level Elements for Object Detection
Building on the success of recent discriminative mid-level elements, we propose a surprisingly simple approach for object detection which performs comparable to the current state-of-the-art approaches on PASCAL VOC comp-3 detection challenge (no external data).
