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SimPoE: Simulated Character Control for 3D Human Pose Estimation

SimPoE: Simulated Character Control for 3D Human Pose Estima...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yuan, Ye Wei, Shih-En Simon, Tomas Kitani, Kris Saragih, Jason Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook Real Labs Redmond WA USA

ISBN: (纸本)9781665445092

Accurate estimation of 3D human motion from monocular video requires modeling both kinematics (body motion without physical forces) and dynamics (motion with physical forces). To demonstrate this, we present SimPoE, a Simulation-based approach for 3D human Pose Estimation, which integrates image-based kinematic inference and physics-based dynamics modeling. SimPoE learns a policy that takes as input the current frame pose estimate and the next image frame to control a physically-simulated character to output the next-frame pose estimate. The policy contains a learnable kinematic pose refinement unit that uses 2D keypoints to iteratively refine its kinematic pose estimate of the next frame. Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e.g., joint torques) of the character to advance the current frame pose estimate to the pose estimate of the next frame. This design couples the kinematic pose refinement unit with the dynamics-based control generation unit, which are learned jointly with reinforcement learning to achieve accurate and physically-plausible pose estimation. Furthermore, we propose a meta-control mechanism that dynamically adjusts the character's dynamics parameters based on the character state to attain more accurate pose estimates. Experiments on large-scale motion datasets demonstrate that our approach establishes the new state of the art in pose accuracy while ensuring physical plausibility.

关键词： Solid modeling computer vision Three-dimensional displays Pose estimation Dynamics Kinematics Reinforcement learning

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Triple-cooperative Video Shadow Detection

Triple-cooperative Video Shadow Detection

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Zhihao Wan, Liang Zhu, Lei Shen, Jia Fu, Huazhu Liu, Wennan Qin, Jing Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Univ Cambridge Dept Appl Math & Theoret Phys Cambridge England Inception Inst Artificial Intelligence Abu Dhabi U Arab Emirates Tianjin Univ Acad Med Engn & Translat Med Tianjin Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Shadow detection in a single image has received significant research interests in recent years. However, much fewer works have been explored in shadow detection over dynamic scenes. The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection. In this work, we collect a new video shadow detection dataset (ViSha), which contains 120 videos with 11;685 frames, covering 60 object categories, varying lengths, and different motion/lighting conditions. All the frames are annotated with a high-quality pixel-level shadow mask. To the best of our knowledge, this is the first learning-oriented dataset for video shadow detection. Furthermore, we develop a new baseline model, named triple-cooperative video shadow detection network (TVSD-Net). It utilizes triple parallel networks in a cooperative manner to learn discriminative representations at intra-video and inter-video levels. Within the network, a dual gated co-attention module is proposed to constrain features from neighboring frames in the same video, while an auxiliary similarity loss is introduced to mine semantic information between different videos. Finally, we conduct a comprehensive study on ViSha, evaluating 12 state-of-the-art models (including single image shadow detectors, video object segmentation, and saliency detection methods). Experiments demonstrate that our model outperforms SOTA competitors.

关键词： computer vision Annotations Semantics Dynamics Object segmentation Detectors Logic gates

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NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Pri...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Wenjing Bian Zirui Wang Kejie Li Jia-Wang Bian Victor Adrian Prisacariu Active Vision Lab University of Oxford

Training a Neural Radiance Field (NeRF) without precomputed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement. We tackle this challenging problem by incorporating undistorted monocular depth priors. These priors are generated by correcting scale and shift parameters during training, with which we are then able to constrain the relative poses between consecutive frames. This constraint is achieved using our proposed novel loss functions. Experiments on real-world indoor and outdoor scenes show that our method can handle challenging camera trajectories and outperforms existing methods in terms of novel view rendering quality and pose estimation accuracy. Our project page is https://***.

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Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

Farewell to Mutual Information: Variational Distillation for...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tian, Xudong Zhang, Zhizhong Lin, Shaohui Qu, Yanyun Xie, Yuan Ma, Lizhuang East China Normal Univ Shanghai Peoples R China Xiamen Univ Xiamen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to viewchanges by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual information.

关键词： Training Correlation Fitting Redundancy Estimation Robustness pattern recognition

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Adaptive Consistency Regularization for Semi-Supervised Transfer Learning

Adaptive Consistency Regularization for Semi-Supervised Tran...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Abuduweili, Abulikemu Li, Xingjian Shi, Humphrey Xu, Cheng-Zhong Dou, Dejing Baidu Res Big Data Lab Beijing Peoples R China Univ Oregon SHI Lab Eugene OR 97403 USA Univ Macau Dept Comp Sci State Key Lab IOTSC Taipa Macao Peoples R China

ISBN: (纸本)9781665445092

While recent studies on semi-supervised learning have shown remarkable progress in leveraging both labeled and unlabeled data, most of them presume a basic setting of the model is randomly initialized. In this work, we consider semi-supervised learning and transfer learning jointly, leading to a more practical and competitive paradigm that can utilize both powerful pre-trained models from source domain as well as labeled/unlabeled data in the target domain. To better exploit the value of both pre-trained weights and unlabeled target examples, we introduce adaptive consistency regularization that consists of two complementary components: Adaptive Knowledge Consistency (AKC) on the examples between the source and target model, and Adaptive Representation Consistency (ARC) on the target model between labeled and unlabeled examples. Examples involved in the consistency regularization are adaptively selected according to their potential contributions to the target task. We conduct extensive experiments on popular benchmarks including CIFAR-10, CUB-200, and MURA, by fine-tuning the ImageNet pre-trained ResNet-50 model. Results show that our proposed adaptive consistency regularization outperforms state-of-the-art semi-supervised learning techniques such as Pseudo Label, Mean Teacher, and FixMatch. Moreover, our algorithm is orthogonal to existing methods and thus able to gain additional improvements on top of MixMatch and Fix Match. Our code is available at https://***/Walleclipse/SemiSupervised-Transfer-Learning-Paddle.

关键词： Adaptation models computer vision Codes Transfer learning Supervised learning Semisupervised learning Benchmark testing

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Adversarial Text to Continuous Image Generation

Adversarial Text to Continuous Image Generation

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conference on computer vision and pattern recognition (cvpr)

作者： Kilichbek Haydarov Aashiq Muhamed Xiaoqian Shen Jovana Lazarevic Ivan Skorokhodov Chamuditha Jayanga Galappaththige Mohamed Elhoseiny King Abdullah University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper, we approach the text-to-image task from a different perspective, where a 2D image is represented as an implicit neural representation (INR). We show that straightforward conditioning of the unconditional INR-based GAN method on text inputs is not enough to achieve good performance. We propose a word-level attention-based weight modulation operator that controls the generation process of INR-GAN based on hypernetworks. Our experiments on benchmark datasets show that HyperCGAN achieves competitive performance to existing pixel-based methods and retains the properties of continuous generative models. Project page link: https://***/webpagelhypercgan.

关键词： Training computer vision Tensors Image synthesis Text to image Modulation Process control

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DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network

DivCo: Diverse Conditional Image Synthesis via Contrastive G...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Rui Ge, Yixiao Choi, Ching Lam Wang, Xiaogang Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China NVIDIA NVIDIA AI Technol Ctr Hong Kong Peoples R China Xidian Univ Sch CST Xian Peoples R China

ISBN: (纸本)9781665445092

Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse. To solve this issue, previous works [47, 22] mainly focused on encouraging the correlation between the latent codes and their generated images, while ignoring the relations between images generated from various latent codes. The recent MSGAN [27] tried to encourage the diversity of the generated image but only considers "negative" relations between the image pairs. In this paper, we propose a novel DivCo framework to properly constrain both "positive" and "negative" relations between the generated images specified in the latent space. To the best of our knowledge, this is the first attempt to use contrastive learning for diverse conditional image synthesis. A novel latent-augmented contrastive loss is introduced, which encourages images generated from adjacent latent codes to be similar and those generated from distinct latent codes to be dissimilar. The proposed latent-augmented contrastive loss is well compatible with various cGAN architectures. Extensive experiments demonstrate that the proposed DivCo can produce more diverse images than state-of-the-art methods without sacrificing visual quality in multiple unpaired and paired image generation tasks. Training code and pretrained models are available at https://***/ruiliu-ai/DivCo.

关键词： Training Visualization computer vision Codes Correlation Image synthesis Generative adversarial networks

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$V_{k}D$: Improving Knowledge Distillation Using Orthogonal Projections

$V_{k}D$: Improving Knowledge Distillation Using Orthogonal ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Roy Miles Ismail Elezi Jiankang Deng Huawei Noah's Ark Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Knowledge distillation is an effective method for training small and efficient deep learning models. However, the efficacy of a single method can degenerate when transferring to other tasks, modalities, or even other architectures. To address this limitation, we propose a novel constrained feature distillation method. This method is derived from a small set of core principles, which results in two emerging components: an orthogonal projection and a task-specific normalisation. Equipped with both of these components, our transformer models can outperform all previous methods on ImageNet and reach up to a 4.4% relative improvement over the previous state-of-the-art methods. To further demonstrate the generality of our method, we apply it to object detection and image generation, whereby we obtain consistent and substantial performance improvements over state-of-the-art. Code and models are publicly available 1 1 https://***/roymiles/vkd.

关键词： Training Deep learning computer vision Image synthesis Object detection Transformer cores Transformers

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Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network

Weakly Supervised Video Emotion Detection and Prediction via...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhicheng Zhang Lijuan Wang Jufeng Yang TMCC College of Computer Science Nankai University China

Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://***/nku-zhichengzhang/WECL.

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Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB

Tuning IR-cut Filter for Illumination-aware Spectral Reconst...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sun, Bo Yan, Junchi Zhou, Xiao Zheng, Yinqiang Univ Southern Calif Los Angeles CA 90007 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Anhui Normal Univ Wuhu Peoples R China Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665445092

To reconstruct spectral signals from multi-channel observations, in particular trichromatic RGBs, has recently emerged as a promising alternative to traditional scanning-based spectral imager. It has been proven that the reconstruction accuracy relies heavily on the spectral response of the RGB camera in use. To improve accuracy, data-driven algorithms have been proposed to retrieve the best response curves of existing RGB cameras, or even to design brand new three-channel response curves. Instead, this paper explores the filter-array based color imaging mechanism of existing RGB cameras, and proposes to design the IR-cut filter properly for improved spectral recovery, which stands out as an in-between solution with better trade-off between reconstruction accuracy and implementation complexity. We further propose a deep learning based spectral reconstruction method, which allows to recover the illumination spectrum as well. Experiment results with both synthetic and real images under daylight illumination have shown the benefits of our IR-cut filter tuning method and our illumination-aware spectral reconstruction method.

关键词： Reflectivity Deep learning computer vision Lighting Reconstruction algorithms Filtering algorithms Cameras

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