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Found 9 papers, 4 papers with code
AvatarMMC: 3D Head Avatar Generation and Editing with Multi-Modal Conditioning
To the best of our knowledge, our approach is the first to introduce multi-modal conditioning to 3D avatar generation and editing.
Text2AC-Zero: Consistent Synthesis of Animated Characters using 2D Diffusion
We propose a zero-shot approach for consistent Text-to-Animated-Characters synthesis based on pre-trained Text-to-Image (T2I) diffusion models.
Zero-Shot 3D Shape Correspondence
Instead, we propose to exploit the in-context learning capabilities of ChatGPT to generate two different sets of semantic regions for each shape and a semantic mapping between them.
Hinge-Wasserstein: Mitigating Overconfidence in Regression by Classification
Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty.
Normalized Convolution Upsampling for Refined Optical Flow Estimation
Our proposed approach formulates the upsampling task as a sparse problem and employs the normalized convolutional neural networks to solve it.
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
In this work, we thus focus on modeling the uncertainty of depth data in depth completion starting from the sparse noisy input all the way to the final prediction.
Unpaired Thermal to Visible Spectrum Transfer using Adversarial Training
When trained and evaluated on KAIST-MS dataset, our proposed methods was shown to produce significantly more realistic and sharp VIS images than the existing state-of-the-art supervised methods.
Confidence Propagation through CNNs for Guided Sparse Depth Regression
In this paper, we propose an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work.
Ranked #7 on
Depth Completion
on KITTI Depth Completion
Propagating Confidences through CNNs for Sparse Data Regression
To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task.
