CVPR 2024 论文列表

Global translation estimation is a highly challenging step in the global structure from motion (SfM) algorithm. Many existing methods depend solely on relative translations leading to inaccuracies in low parallax scenes and degradation under collinear camera motion. While recent approaches aim to address these issues by incorporating feature tracks into objective functions they are often sensitive to outliers. In this paper we first revisit global translation estimation methods with feature tracks and categorize them into explicit and implicit methods. Then we highlight the superiority of the objective function based on the cross-product distance metric and propose a novel explicit global translation estimation framework that integrates both relative translations and feature tracks as input. To enhance the accuracy of input observations we re-estimate relative translations with the coplanarity constraint of the epipolar plane and propose a simple yet effective strategy to select reliable feature tracks. Finally the effectiveness of our approach is demonstrated through experiments on urban image sequences and unordered Internet images showcasing its superior accuracy and robustness compared to many state-of-the-art techniques.

Sign Language Translation (SLT) is a challenging task that aims to translate sign videos into spoken language. Inspired by the strong translation capabilities of large language models (LLMs) that are trained on extensive multilingual text corpora we aim to harness off-the-shelf LLMs to handle SLT. In this paper we regularize the sign videos to embody linguistic characteristics of spoken language and propose a novel SignLLM framework to transform sign videos into a language-like representation for improved readability by off-the-shelf LLMs. SignLLM comprises two key modules: (1) The Vector-Quantized Visual Sign module converts sign videos into a sequence of discrete character-level sign tokens and (2) the Codebook Reconstruction and Alignment module converts these character-level tokens into word-level sign representations using an optimal transport formulation. A sign-text alignment loss further bridges the gap between sign and text tokens enhancing semantic compatibility. We achieve state-of-the-art gloss-free results on two widely-used SLT benchmarks.

We present GenN2N a unified NeRF-to-NeRF translation framework for various NeRF translation tasks such as text-driven NeRF editing colorization super-resolution inpainting etc. Unlike previous methods designed for individual translation tasks with task-specific schemes GenN2N achieves all these NeRF editing tasks by employing a plug-and-play image-to-image translator to perform editing in the 2D domain and lifting 2D edits into the 3D NeRF space. Since the 3D consistency of 2D edits may not be assured we propose to model the distribution of the underlying 3D edits through a generative model that can cover all possible edited NeRFs. To model the distribution of 3D edited NeRFs from 2D edited images we carefully design a VAE-GAN that encodes images while decoding NeRFs. The latent space is trained to align with a Gaussian distribution and the NeRFs are supervised through an adversarial loss on its renderings. To ensure the latent code does not depend on 2D viewpoints but truly reflects the 3D edits we also regularize the latent code through a contrastive learning scheme. Extensive experiments on various editing tasks show GenN2N as a universal framework performs as well or better than task-specific specialists while possessing flexible generative power. More results on our project page: https://xiangyueliu.github.io/GenN2N/.