Search Results for author:
Found 9 papers, 7 papers with code
Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction
However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes.
An Implicit Alignment for Video Super-Resolution
Video super-resolution commonly uses a frame-wise alignment to support the propagation of information over time.
Ranked #1 on
Video Super-Resolution
on REDS4- 4x upscaling
DepGraph: Towards Any Structural Pruning
Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks.
Bias-Compensated Integral Regression for Human Pose Estimation
Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.
Improving Deep Regression with Ordinal Entropy
In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance.
Ranked #9 on
Monocular Depth Estimation
on NYU-Depth V2
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
In our DA framework, we retain the depth and background information during the domain feature alignment.
ONCE-3DLanes: Building Monocular 3D Lane Detection
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision
Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses.
Ranked #15 on
3D Human Pose Estimation
on MPI-INF-3DHP
Point2Seq: Detecting 3D Objects as Sequences
We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words.
