CVPR 2024 论文列表

Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While significant progress has been made we reveal that state-of-the-art ETL approaches exhibit strong performance only in narrowly-defined experimental setups and with a careful adjustment of hyperparameters based on a large corpus of labeled samples. In particular we make two interesting and surprising empirical observations. First to outperform a simple Linear Probing baseline these methods require to optimize their hyper-parameters on each target task. And second they typically underperform --sometimes dramatically-- standard zero-shot predictions in the presence of distributional drifts. Motivated by the unrealistic assumptions made in the existing literature i.e. access to a large validation set and case-specific grid-search for optimal hyperparameters we propose a novel approach that meets the requirements of real-world scenarios. More concretely we introduce a CLass-Adaptive linear Probe (CLAP) objective whose balancing term is optimized via an adaptation of the general Augmented Lagrangian method tailored to this context. We comprehensively evaluate CLAP on a broad span of datasets and scenarios demonstrating that it consistently outperforms SoTA approaches while yet being a much more efficient alternative.

The lifting of a 3D structure and camera from 2D landmarks is at the cornerstone of the discipline of computer vision. Traditional methods have been confined to specific rigid objects such as those in Perspective-n-Point (PnP) problems but deep learning has expanded our capability to reconstruct a wide range of object classes (e.g. C3DPO [??] and PAUL [??]) with resilience to noise occlusions and perspective distortions. However all these techniques have been limited by the fundamental need to establish correspondences across the 3D training data significantly limiting their utility to applications where one has an abundance of "in-correspondence" 3D data. Our approach harnesses the inherent permutation equivariance of transformers to manage varying numbers of points per 3D data instance withstands occlusions and generalizes to unseen categories. We demonstrate state-of-the-art performance across 2D-3D lifting task benchmarks. Since our approach can be trained across such a broad class of structures we refer to it simply as a 3D Lifting Foundation Model (3D-LFM) -- the first of its kind.

In this paper we present RStab a novel framework for video stabilization that integrates 3D multi-frame fusion through volume rendering. Departing from conventional methods we introduce a 3D multi-frame perspective to generate stabilized images addressing the challenge of full-frame generation while preserving structure. The core of our RStab framework lies in Stabilized Rendering (SR) a volume rendering module fusing multi-frame information in 3D space. Specifically SR involves warping features and colors from multiple frames by projection fusing them into descriptors to render the stabilized image. However the precision of warped information depends on the projection accuracy a factor significantly influenced by dynamic regions. In response we introduce the Adaptive Ray Range (ARR) module to integrate depth priors adaptively defining the sampling range for the projection process. Additionally we propose Color Correction (CC) assisting geometric constraints with optical flow for accurate color aggregation. Thanks to the three modules our RStab demonstrates superior performance compared with previous stabilizers in the field of view (FOV) image quality and video stability across various datasets.

When working with 3D facial data improving fidelity and avoiding the uncanny valley effect is critically dependent on accurate 3D facial performance capture. Because such methods are expensive and due to the widespread availability of 2D videos recent methods have focused on how to perform monocular 3D face tracking. However these methods often fall short in capturing precise facial movements due to limitations in their network architecture training and evaluation processes. Addressing these challenges we propose a novel face tracker FlowFace that introduces an innovative 2D alignment network for dense per-vertex alignment. Unlike prior work FlowFace is trained on high-quality 3D scan annotations rather than weak supervision or synthetic data. Our 3D model fitting module jointly fits a 3D face model from one or many observations integrating existing neutral shape priors for enhanced identity and expression disentanglement and per-vertex deformations for detailed facial feature reconstruction. Additionally we propose a novel metric and benchmark for assessing tracking accuracy. Our method exhibits superior performance on both custom and publicly available benchmarks. We further validate the effectiveness of our tracker by generating high-quality 3D data from 2D videos which leads to performance gains on downstream tasks.

Portable 360^\circ cameras are becoming a cheap and efficient tool to establish large visual databases. By capturing omnidirectional views of a scene these cameras could expedite building environment models that are essential for visual localization. However such an advantage is often overlooked due to the lack of valuable datasets. This paper introduces a new benchmark dataset 360Loc composed of 360^\circ images with ground truth poses for visual localization. We present a practical implementation of 360^\circ mapping combining 360^\circ images with lidar data to generate the ground truth 6DoF poses. 360Loc is the first dataset and benchmark that explores the challenge of cross-device visual positioning involving 360^\circ reference frames and query frames from pinhole ultra-wide FoV fisheye and 360^\circ cameras. We propose a virtual camera approach to generate lower-FoV query frames from 360^\circ images which ensures a fair comparison of performance among different query types in visual localization tasks. We also extend this virtual camera approach to feature matching-based and pose regression-based methods to alleviate the performance loss caused by the cross-device domain gap and evaluate its effectiveness against state-of-the-art baselines. We demonstrate that omnidirectional visual localization is more robust in challenging large-scale scenes with symmetries and repetitive structures. These results provide new insights into 360-camera mapping and omnidirectional visual localization with cross-device queries. Project Page and dataset: https://huajianup.github.io/research/360Loc/.