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Found 15 papers, 3 papers with code
NCRF: Neural Contact Radiance Fields for Free-Viewpoint Rendering of Hand-Object Interaction
Despite remarkable progress that has been achieved in this field, existing methods still fail to synthesize the hand-object interaction photo-realistically, suffering from degraded rendering quality caused by the heavy mutual occlusions between the hand and the object, and inaccurate hand-object pose estimation.
Deformable 3D Gaussian Splatting for Animatable Human Avatars
ParDy-Human introduces parameter-driven dynamics into 3D Gaussian Splatting where 3D Gaussians are deformed by a human pose model to animate the avatar.
SWAGS: Sampling Windows Adaptively for Dynamic 3D Gaussian Splatting
We model the dynamics of a scene using a tunable MLP, which learns the deformation field from a canonical space to a set of 3D Gaussians per frame.
Reality's Canvas, Language's Brush: Crafting 3D Avatars from Monocular Video
A pose-conditioned deformable NeRF is optimized to volumetrically represent a human subject in canonical T-pose.
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fields.
Human Gaussian Splatting: Real-time Rendering of Animatable Avatars
This work addresses the problem of real-time rendering of photorealistic human body avatars learned from multi-view videos.
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data.
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression
Depth estimation is a core task in 3D computer vision.
CroMo: Cross-Modal Learning for Monocular Depth Estimation
Learning-based depth estimation has witnessed recent progress in multiple directions; from self-supervision using monocular video to supervised methods offering highest accuracy.
Generalizable Person Re-Identification by Domain-Invariant Mapping Network
We aim to learn a domain generalizable person re-identification (ReID) model.
Domain Generalization
Generalizable Person Re-identification
+1
Deep Factorised Inverse-Sketching
Instead there is a fundamental process of abstraction and iconic rendering, where overall geometry is warped and salient details are selectively included.
Learning to Sketch with Shortcut Cycle Consistency
In this paper, we present a novel approach for translating an object photo to a sketch, mimicking the human sketching process.
Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval
Human sketches are unique in being able to capture both the spatial topology of a visual object, as well as its subtle appearance details.
Ranked #2 on
Sketch-Based Image Retrieval
on Handbags
