mtybilly/Instruct2DS huggingface dataset:
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors
Authors: Lihe Ding, Shaocong Dong, Zhanpeng Huang, Zibin Wang, Yiyuan Zhang, Kaixiong Gong, Dan Xu, Tianfan Xue
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors these methods often lead to geometric anomalies and multi-view inconsistency. Recently researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets. To harness the advantages of both approaches we propose Bidirectional Diffusion (BiDiff) a unified framework that incorporates both a 3D and a 2D diffusion process to preserve both 3D fidelity and 2D texture richness respectively. Moreover as a simple combination may yield inconsistent generation results we further bridge them with novel bidirectional guidance. In addition our method can be used as an initialization of optimization-based models to further improve the quality of 3D model and efficiency of optimization reducing the process from 3.4 hours to 20 minutes. Experimental results have shown that our model achieves high-quality diverse and scalable 3D generation. Project website https://bidiff.github.io/.
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning
Authors: Lianggangxu Chen, Xuejiao Wang, Jiale Lu, Shaohui Lin, Changbo Wang, Gaoqi He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3D Scene Graph Generation (3DSGG) aims to classify objects and their predicates within 3D point cloud scenes. However current 3DSGG methods struggle with two main challenges. 1) The dependency on labor-intensive ground-truth annotations. 2) Closed-set classes training hampers the recognition of novel objects and predicates. Addressing these issues our idea is to extract cross-modality features by CLIP from text and image data naturally related to 3D point clouds. Cross-modality features are used to train a robust 3D scene graph (3DSG) feature extractor. Specifically we propose a novel Cross-Modality Contrastive Learning 3DSGG (CCL-3DSGG) method. Firstly to align the text with 3DSG the text is parsed into word level that are consistent with the 3DSG annotation. To enhance robustness during the alignment adjectives are exchanged for different objects as negative samples. Then to align the image with 3DSG the camera view is treated as a positive sample and other views as negatives. Lastly the recognition of novel object and predicate classes is achieved by calculating the cosine similarity between prompts and 3DSG features. Our rigorous experiments confirm the superior open-vocabulary capability and applicability of CCL-3DSGG in real-world contexts.
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving
Authors: Mozhgan Pourkeshavarz, Mohammad Sabokrou, Amir Rasouli
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In autonomous driving behavior prediction is fundamental for safe motion planning hence the security and robustness of prediction models against adversarial attacks are of paramount importance. We propose a novel adversarial backdoor attack against trajectory prediction models as a means of studying their potential vulnerabilities. Our attack affects the victim at training time via naturalistic hence stealthy poisoned samples crafted using a novel two-step approach. First the triggers are crafted by perturbing the trajectory of attacking vehicle and then disguised by transforming the scene using a bi-level optimization technique. The proposed attack does not depend on a particular model architecture and operates in a black-box manner thus can be effective without any knowledge of the victim model. We conduct extensive empirical studies using state-of-the-art prediction models on two benchmark datasets using metrics customized for trajectory prediction. We show that the proposed attack is highly effective as it can significantly hinder the performance of prediction models unnoticeable by the victims and efficient as it forces the victim to generate malicious behavior even under constrained conditions. Via ablative studies we analyze the impact of different attack design choices followed by an evaluation of existing defence mechanisms against the proposed attack.
Make-It-Vivid: Dressing Your Animatable Biped Cartoon Characters from Text
Authors: Junshu Tang, Yanhong Zeng, Ke Fan, Xuheng Wang, Bo Dai, Kai Chen, Lizhuang Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Creating and animating 3D biped cartoon characters is crucial and valuable in various applications. Compared with geometry the diverse texture design plays an important role in making 3D biped cartoon characters vivid and charming. Therefore we focus on automatic texture design for cartoon characters based on input instructions. This is challenging for domain-specific requirements and a lack of high-quality data. To address this challenge we propose Make-It-Vivid the first attempt to enable high-quality texture generation from text in UV space. We prepare a detailed text-texture paired data for 3D characters by using vision-question-answering agents. Then we customize a pretrained text-to-image model to generate texture map with template structure while preserving the natural 2D image knowledge. Furthermore to enhance fine-grained details we propose a novel adversarial learning scheme to shorten the domain gap between original dataset and realistic texture domain. Extensive experiments show that our approach outperforms current texture generation methods resulting in efficient character texturing and faithful generation with prompts. Besides we showcase various applications such as out of domain generation and texture stylization. We also provide an efficient generation system for automatic text-guided textured character generation and animation.
StraightPCF: Straight Point Cloud Filtering
Authors: Dasith de Silva Edirimuni, Xuequan Lu, Gang Li, Lei Wei, Antonio Robles-Kelly, Hongdong Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Point cloud filtering is a fundamental 3D vision task which aims to remove noise while recovering the underlying clean surfaces. State-of-the-art methods remove noise by moving noisy points along stochastic trajectories to the clean surfaces. These methods often require regularization within the training objective and/or during post-processing to ensure fidelity. In this paper we introduce StraightPCF a new deep learning based method for point cloud filtering. It works by moving noisy points along straight paths thus reducing discretization errors while ensuring faster convergence to the clean surfaces. We model noisy patches as intermediate states between high noise patch variants and their clean counterparts and design the VelocityModule to infer a constant flow velocity from the former to the latter. This constant flow leads to straight filtering trajectories. In addition we introduce a DistanceModule that scales the straight trajectory using an estimated distance scalar to attain convergence near the clean surface. Our network is lightweight and only has  530K parameters being 17% of IterativePFN (a most recent point cloud filtering network). Extensive experiments on both synthetic and real-world data show our method achieves state-of-the-art results. Our method also demonstrates nice distributions of filtered points without the need for regularization. The implementation code can be found at: https://github.com/ddsediri/StraightPCF.
Mirasol3B: A Multimodal Autoregressive Model for Time-Aligned and Contextual Modalities
Authors: AJ Piergiovanni, Isaac Noble, Dahun Kim, Michael S. Ryoo, Victor Gomes, Anelia Angelova
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: One of the main challenges of multimodal learning is the need to combine heterogeneous modalities (e.g. video audio text). For example video and audio are obtained at much higher rates than text and are roughly aligned in time. They are often not synchronized with text which comes as a global context e.g. a title or a description. Furthermore video and audio inputs are of much larger volumes and grow as the video length increases which naturally requires more compute dedicated to these modalities and makes modeling of long-range dependencies harder. We here decouple the multimodal modeling dividing it into separate autoregressive models processing the inputs according to the characteristics of the modalities. We propose a multimodal model consisting of an autoregressive component for the time-synchronized modalities (audio and video) and an autoregressive component for the context modalities which are not necessarily aligned in time but are still sequential. To address the long-sequences of the video-audio inputs we further partition the video and audio sequences in consecutive snippets and autoregressively process their representations. To that end we propose a Combiner mechanism which models the audio-video information jointly producing compact but expressive representations. This allows us to scale to 512 input video frames without increase in model parameters. Our approach achieves the state-of-the-art on multiple well established multimodal benchmarks. It effectively addresses the high computational demand of media inputs by learning compact representations controlling the sequence length of the audio-video feature representations and modeling their dependencies in time.
Neural Sign Actors: A Diffusion Model for 3D Sign Language Production from Text
Authors: Vasileios Baltatzis, Rolandos Alexandros Potamias, Evangelos Ververas, Guanxiong Sun, Jiankang Deng, Stefanos Zafeiriou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Sign Languages (SL) serve as the primary mode of communication for the Deaf and Hard of Hearing communities. Deep learning methods for SL recognition and translation have achieved promising results. However Sign Language Production (SLP) poses a challenge as the generated motions must be realistic and have precise semantic meaning. Most SLP methods rely on 2D data which hinders their realism. In this work a diffusion-based SLP model is trained on a curated large-scale dataset of 4D signing avatars and their corresponding text transcripts. The proposed method can generate dynamic sequences of 3D avatars from an unconstrained domain of discourse using a diffusion process formed on a novel and anatomically informed graph neural network defined on the SMPL-X body skeleton. Through quantitative and qualitative experiments we show that the proposed method considerably outperforms previous methods of SLP. This work makes an important step towards realistic neural sign avatars bridging the communication gap between Deaf and hearing communities.
On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
Authors: Peng Sun, Bei Shi, Daiwei Yu, Tao Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Contemporary machine learning which involves training large neural networks on massive datasets faces significant computational challenges. Dataset distillation as a recent emerging strategy aims to compress real-world datasets for efficient training. However this line of research currently struggles with large-scale and high-resolution datasets hindering its practicality and feasibility. Thus we re-examine existing methods and identify three properties essential for real-world applications: realism diversity and efficiency. As a remedy we propose RDED a novel computationally-efficient yet effective data distillation paradigm to enable both diversity and realism of the distilled data. Extensive empirical results over various model architectures and datasets demonstrate the advancement of RDED: we can distill a dataset to 10 images per class from full ImageNet-1K within 7 minutes achieving a notable 42% accuracy with ResNet-18 on a single RTX-4090 GPU (while the SOTA only achieves 21% but requires 6 hours). Code: https://github.com/LINs-lab/RDED.
Semantics-aware Motion Retargeting with Vision-Language Models
Authors: Haodong Zhang, Zhike Chen, Haocheng Xu, Lei Hao, Xiaofei Wu, Songcen Xu, Zhensong Zhang, Yue Wang, Rong Xiong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Capturing and preserving motion semantics is essential to motion retargeting between animation characters. However most of the previous works neglect the semantic information or rely on human-designed joint-level representations. Here we present a novel Semantics-aware Motion reTargeting (SMT) method with the advantage of vision-language models to extract and maintain meaningful motion semantics. We utilize a differentiable module to render 3D motions. Then the high-level motion semantics are incorporated into the motion retargeting process by feeding the vision-language model with the rendered images and aligning the extracted semantic embeddings. To ensure the preservation of fine-grained motion details and high-level semantics we adopt a two-stage pipeline consisting of skeleton-aware pre-training and fine-tuning with semantics and geometry constraints. Experimental results show the effectiveness of the proposed method in producing high-quality motion retargeting results while accurately preserving motion semantics. Project page can be found at https://sites.google.com/view/smtnet.
Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer
Authors: Yuwen Tan, Qinhao Zhou, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Class-incremental learning (CIL) aims to enable models to continuously learn new classes while overcoming catastrophic forgetting. The introduction of pre-trained models has brought new tuning paradigms to CIL. In this paper we revisit different parameter-efficient tuning (PET) methods within the context of continual learning. We observe that adapter tuning demonstrates superiority over prompt-based methods even without parameter expansion in each learning session. Motivated by this we propose incrementally tuning the shared adapter without imposing parameter update constraints enhancing the learning capacity of the backbone. Additionally we employ feature sampling from stored prototypes to retrain a unified classifier further improving its performance. We estimate the semantic shift of old prototypes without access to past samples and update stored prototypes session by session. Our proposed method eliminates model expansion and avoids retaining any image samples. It surpasses previous pre-trained model-based CIL methods and demonstrates remarkable continual learning capabilities. Experimental results on five CIL benchmarks validate the effectiveness of our approach achieving state-of-the-art (SOTA) performance.
Low-Rank Approximation for Sparse Attention in Multi-Modal LLMs
Authors: Lin Song, Yukang Chen, Shuai Yang, Xiaohan Ding, Yixiao Ge, Ying-Cong Chen, Ying Shan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper focuses on the high computational complexity in Large Language Models (LLMs) a significant challenge in both natural language processing (NLP) and multi-modal tasks. We propose Low-Rank Approximation for Sparse At- tention (LoRA-Sparse) an innovative approach that strate- gically reduces this complexity. LoRA-Sparse introduces low-rank linear projection layers for sparse attention ap- proximation. It utilizes an order-mimic training methodol- ogy which is crucial for efficiently approximating the self- attention mechanism in LLMs. We empirically show that sparse attention not only reduces computational demands but also enhances model performance in both NLP and multi-modal tasks. This surprisingly shows that redundant attention in LLMs might be non-beneficial. We extensively validate LoRA-Sparse through rigorous empirical studies in both (NLP) and multi-modal tasks demonstrating its effec- tiveness and general applicability. Based on LLaMA and LLaVA models our methods can reduce more than half of the self-attention computation with even better performance than full-attention baselines.
TASeg: Temporal Aggregation Network for LiDAR Semantic Segmentation
Authors: Xiaopei Wu, Yuenan Hou, Xiaoshui Huang, Binbin Lin, Tong He, Xinge Zhu, Yuexin Ma, Boxi Wu, Haifeng Liu, Deng Cai, Wanli Ouyang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Training deep models for LiDAR semantic segmentation is challenging due to the inherent sparsity of point clouds. Utilizing temporal data is a natural remedy against the sparsity problem as it makes the input signal denser. However previous multi-frame fusion algorithms fall short in utilizing sufficient temporal information due to the memory constraint and they also ignore the informative temporal images. To fully exploit rich information hidden in long-term temporal point clouds and images we present the Temporal Aggregation Network termed TASeg. Specifically we propose a Temporal LiDAR Aggregation and Distillation (TLAD) algorithm which leverages historical priors to assign different aggregation steps for different classes. It can largely reduce memory and time overhead while achieving higher accuracy. Besides TLAD trains a teacher injected with gt priors to distill the model further boosting the performance. To make full use of temporal images we design a Temporal Image Aggregation and Fusion (TIAF) module which can greatly expand the camera FOV and enhance the present features. Temporal LiDAR points in the camera FOV are used as mediums to transform temporal image features to the present coordinate for temporal multi-modal fusion. Moreover we develop a Static-Moving Switch Augmentation (SMSA) algorithm which utilizes sufficient temporal information to enable objects to switch their motion states freely thus greatly increasing static and moving training samples. Our TASeg ranks 1st on three challenging tracks i.e. SemanticKITTI single-scan track multi-scan track and nuScenes LiDAR segmentation track strongly demonstrating the superiority of our method. Codes are available at https://github.com/LittlePey/TASeg.
Bootstrapping SparseFormers from Vision Foundation Models
Authors: Ziteng Gao, Zhan Tong, Kevin Qinghong Lin, Joya Chen, Mike Zheng Shou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The recently proposed SparseFormer architecture provides an alternative approach to visual understanding by utilizing a significantly lower number of visual tokens via adjusting RoIs greatly reducing computational costs while still achieving promising performance. However training SparseFormers from scratch is still expensive and scaling up the number of parameters can be challenging. In this paper we propose to bootstrap SparseFormers from ViT-based vision foundation models in a simple and efficient way. Since the majority of SparseFormer blocks are the standard transformer ones we can inherit weights from large-scale pre-trained vision transformers and freeze them as much as possible. Therefore we only need to train the SparseFormer-specific lightweight focusing transformer to adjust token RoIs and fine-tune a few early pre-trained blocks to align the final token representation. In such a way we can bootstrap SparseFormer architectures from various large-scale pre-trained models (e.g. IN-21K pre-trained AugRegs or CLIPs) using a rather smaller amount of training samples (e.g. IN-1K) and without labels or captions within just a few hours. As a result the bootstrapped unimodal SparseFormer (from AugReg-ViT-L/16-384) can reach 84.9% accuracy on IN-1K with only 49 tokens and the multimodal SparseFormer from CLIPs also demonstrates notable zero-shot performance with highly reduced computational cost without seeing any caption during the bootstrapping procedure. In addition CLIP-bootstrapped SparseFormers which align the output space with language without seeing a word can serve as efficient vision encoders in multimodal large language models. Code and models are available at https://github.com/showlab/sparseformer
EventPS: Real-Time Photometric Stereo Using an Event Camera
Authors: Bohan Yu, Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Photometric stereo is a well-established technique to estimate the surface normal of an object. However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency. EventPS seamlessly integrates with both optimization-based and deep-learning-based photometric stereo techniques to offer a robust solution for non-Lambertian surfaces. Extensive experiments validate the effectiveness and efficiency of EventPS compared to frame-based counterparts. Our algorithm runs at over 30 fps in real-world scenarios unleashing the potential of EventPS in time-sensitive and high-speed downstream applications.
Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
Authors: Leon Sick, Dominik Engel, Pedro Hermosilla, Timo Ropinski
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Traditionally training neural networks to perform semantic segmentation requires expensive human-made annotations. But more recently advances in the field of unsupervised learning have made significant progress on this issue and towards closing the gap to supervised algorithms. To achieve this semantic knowledge is distilled by learning to correlate randomly sampled features from images across an entire dataset. In this work we build upon these advances by incorporating information about the structure of the scene into the training process through the use of depth information. We achieve this by (1) learning depth-feature correlation by spatially correlating the feature maps with the depth maps to induce knowledge about the structure of the scene and (2) exploiting farthest-point sampling to more effectively select relevant features by utilizing 3D sampling techniques on depth information of the scene. Finally we demonstrate the effectiveness of our technical contributions through extensive experimentation and present significant improvements in performance across multiple benchmark datasets.
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving
Authors: Kaituo Feng, Changsheng Li, Dongchun Ren, Ye Yuan, Guoren Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: End-to-end motion planning models equipped with deep neural networks have shown great potential for enabling full autonomous driving. However the oversized neural networks render them impractical for deployment on resource-constrained systems which unavoidably requires more computational time and resources during reference. To handle this knowledge distillation offers a promising approach that compresses models by enabling a smaller student model to learn from a larger teacher model. Nevertheless how to apply knowledge distillation to compress motion planners has not been explored so far. In this paper we propose PlanKD the first knowledge distillation framework tailored for compressing end-to-end motion planners. First considering that driving scenes are inherently complex often containing planning-irrelevant or even noisy information transferring such information is not beneficial for the student planner. Thus we design an information bottleneck based strategy to only distill planning-relevant information rather than transfer all information indiscriminately. Second different waypoints in an output planned trajectory may hold varying degrees of importance for motion planning where a slight deviation in certain crucial waypoints might lead to a collision. Therefore we devise a safety-aware waypoint-attentive distillation module that assigns adaptive weights to different waypoints based on the importance to encourage the student to accurately mimic more crucial waypoints thereby improving overall safety. Experiments demonstrate that our PlanKD can boost the performance of smaller planners by a large margin and significantly reduce their reference time.
RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models
Authors: Ozgur Kara, Bariscan Kurtkaya, Hidir Yesiltepe, James M. Rehg, Pinar Yanardag
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advancements in diffusion-based models have demonstrated significant success in generating images from text. However video editing models have not yet reached the same level of visual quality and user control. To address this we introduce RAVE a zero-shot video editing method that leverages pre-trained text-to-image diffusion models without additional training. RAVE takes an input video and a text prompt to produce high-quality videos while preserving the original motion and semantic structure. It employs a novel noise shuffling strategy leveraging spatio-temporal interactions between frames to produce temporally consistent videos faster than existing methods. It is also efficient in terms of memory requirements allowing it to handle longer videos. RAVE is capable of a wide range of edits from local attribute modifications to shape transformations. In order to demonstrate the versatility of RAVE we create a comprehensive video evaluation dataset ranging from object-focused scenes to complex human activities like dancing and typing and dynamic scenes featuring swimming fish and boats. Our qualitative and quantitative experiments highlight the effectiveness of RAVE in diverse video editing scenarios compared to existing methods. Our code dataset and videos can be found in \href https://rave-video-edit.github.io/.
PredToken: Predicting Unknown Tokens and Beyond with Coarse-to-Fine Iterative Decoding
Authors: Xuesong Nie, Haoyuan Jin, Yunfeng Yan, Xi Chen, Zhihang Zhu, Donglian Qi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Predictive learning models which aim to predict future frames based on past observations are crucial to constructing world models. These models need to maintain low-level consistency and capture high-level dynamics in unannotated spatiotemporal data. Transitioning from frame-wise to token-wise prediction presents a viable strategy for addressing these needs. How to improve token representation and optimize token decoding presents significant challenges. This paper introduces PredToken a novel predictive framework that addresses these issues by decoupling space-time tokens into distinct components for iterative cascaded decoding. Concretely we first design a "decomposition quantization and reconstruction" schema based on VQGAN to improve the token representation. This scheme disentangles low- and high-frequency representations and employs a dimension-aware quantization model allowing more low-level details to be preserved. Building on this we present a "coarse-to-fine iterative decoding" method. It leverages dynamic soft decoding to refine coarse tokens and static soft decoding for fine tokens enabling more high-level dynamics to be captured. These designs make PredToken produce high-quality predictions. Extensive experiments demonstrate the superiority of our method on various real-world spatiotemporal predictive benchmarks. Furthermore PredToken can also be extended to other visual generative tasks to yield realistic outcomes.
Video-Based Human Pose Regression via Decoupled Space-Time Aggregation
Authors: Jijie He, Wenwu Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: By leveraging temporal dependency in video sequences multi-frame human pose estimation algorithms have demonstrated remarkable results in complicated situations such as occlusion motion blur and video defocus. These algorithms are predominantly based on heatmaps resulting in high computation and storage requirements per frame which limits their flexibility and real-time application in video scenarios particularly on edge devices. In this paper we develop an efficient and effective video-based human pose regression method which bypasses intermediate representations such as heatmaps and instead directly maps the input to the output joint coordinates. Despite the inherent spatial correlation among adjacent joints of the human pose the temporal trajectory of each individual joint exhibits relative independence. In light of this we propose a novel Decoupled Space-Time Aggregation network (DSTA) to separately capture the spatial contexts between adjacent joints and the temporal cues of each individual joint thereby avoiding the conflation of spatiotemporal dimensions. Concretely DSTA learns a dedicated feature token for each joint to facilitate the modeling of their spatiotemporal dependencies. With the proposed joint-wise local-awareness attention mechanism our method is capable of efficiently and flexibly utilizing the spatial dependency of adjacent joints and the temporal dependency of each joint itself. Extensive experiments demonstrate the superiority of our method. Compared to previous regression-based single-frame human pose estimation methods DSTA significantly enhances performance achieving an 8.9 mAP improvement on PoseTrack2017. Furthermore our approach either surpasses or is on par with the state-of-the-art heatmap-based multi-frame human pose estimation methods. Project page: https://github.com/zgspose/DSTA.
L-MAGIC: Language Model Assisted Generation of Images with Coherence
Authors: Zhipeng Cai, Matthias Mueller, Reiner Birkl, Diana Wofk, Shao-Yen Tseng, Junda Cheng, Gabriela Ben-Melech Stan, Vasudev Lai, Michael Paulitsch
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In the current era of generative AI breakthroughs generating panoramic scenes from a single input image remains a key challenge. Most existing methods use diffusion-based iterative or simultaneous multi-view inpainting. However the lack of global scene layout priors leads to subpar outputs with duplicated objects (e.g. multiple beds in a bedroom) or requires time-consuming human text inputs for each view. We propose L-MAGIC a novel method leveraging large language models for guidance while diffusing multiple coherent views of 360 degree panoramic scenes. L-MAGIC harnesses pre-trained diffusion and language models without fine-tuning ensuring zero-shot performance. The output quality is further enhanced by super-resolution and multi-view fusion techniques. Extensive experiments demonstrate that the resulting panoramic scenes feature better scene layouts and perspective view rendering quality compared to related works with >70% preference in human evaluations. Combined with conditional diffusion models L-MAGIC can accept various input modalities including but not limited to text depth maps sketches and colored scripts. Applying depth estimation further enables 3D point cloud generation and dynamic scene exploration with fluid camera motion.
3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow
Authors: Felix Taubner, Prashant Raina, Mathieu Tuli, Eu Wern Teh, Chul Lee, Jinmiao Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning
Authors: Desai Xie, Jiahao Li, Hao Tan, Xin Sun, Zhixin Shu, Yi Zhou, Sai Bi, Sören Pirk, Arie E. Kaufman
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multi-view diffusion models obtained by applying Supervised Finetuning (SFT) to text-to-image diffusion models have driven recent breakthroughs in text-to-3D research. However due to the limited size and quality of existing 3D datasets they still suffer from multi-view inconsistencies and Neural Radiance Field (NeRF) reconstruction artifacts. We argue that multi-view diffusion models can benefit from further Reinforcement Learning Finetuning (RLFT) which allows models to learn from the data generated by themselves and improve beyond their dataset limitations during SFT. To this end we introduce Carve3D an improved RLFT algorithm coupled with a novel Multi-view Reconstruction Consistency (MRC) metric to enhance the consistency of multi-view diffusion models. To measure the MRC metric on a set of multi-view images we compare them with their corresponding NeRF renderings at the same camera viewpoints. The resulting model which we denote as Carve3DM demonstrates superior multi-view consistency and NeRF reconstruction quality than existing models. Our results suggest that pairing SFT with Carve3D's RLFT is essential for developing multi-view-consistent diffusion models mirroring the standard Large Language Model (LLM) alignment pipeline. Our code training and testing data and video results are available at: https://desaixie.github.io/carve-3d.
Random Entangled Tokens for Adversarially Robust Vision Transformer
Authors: Huihui Gong, Minjing Dong, Siqi Ma, Seyit Camtepe, Surya Nepal, Chang Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks (CNNs) in the realm of computer vision showcasing tremendous potential. However recent research has unveiled a susceptibility of ViTs to adversarial attacks akin to their CNN counterparts. Adversarial training and randomization are two representative effective defenses for CNNs. Some researchers have attempted to apply adversarial training to ViTs and achieved comparable robustness to CNNs while it is not easy to directly apply randomization to ViTs because of the architecture difference between CNNs and ViTs. In this paper we delve into the structural intricacies of ViTs and propose a novel defense mechanism termed Random entangled image Transformer (ReiT) which seamlessly integrates adversarial training and randomization to bolster the adversarial robustness of ViTs. Recognizing the challenge posed by the structural disparities between ViTs and CNNs we introduce a novel module input-independent random entangled self-attention (II-ReSA). This module optimizes random entangled tokens that lead to "dissimilar" self-attention outputs by leveraging model parameters and the sampled random tokens thereby synthesizing the self-attention module outputs and random entangled tokens to diminish adversarial similarity. ReiT incorporates two distinct random entangled tokens and employs dual randomization offering an effective countermeasure against adversarial examples while ensuring comprehensive deduction guarantees. Through extensive experiments conducted on various ViT variants and benchmarks we substantiate the superiority of our proposed method in enhancing the adversarial robustness of Vision Transformers.
Shadow Generation for Composite Image Using Diffusion Model
Authors: Qingyang Liu, Junqi You, Jianting Wang, Xinhao Tao, Bo Zhang, Li Niu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In the realm of image composition generating realistic shadow for the inserted foreground remains a formidable challenge. Previous works have developed image-to-image translation models which are trained on paired training data. However they are struggling to generate shadows with accurate shapes and intensities hindered by data scarcity and inherent task complexity. In this paper we resort to foundation model with rich prior knowledge of natural shadow images. Specifically we first adapt ControlNet to our task and then propose intensity modulation modules to improve the shadow intensity. Moreover we extend the small-scale DESOBA dataset to DESOBAv2 using a novel data acquisition pipeline. Experimental results on both DESOBA and DESOBAv2 datasets as well as real composite images demonstrate the superior capability of our model for shadow generation task. The dataset code and model are released at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2.
DisCo: Disentangled Control for Realistic Human Dance Generation
Authors: Tan Wang, Linjie Li, Kevin Lin, Yuanhao Zhai, Chung-Ching Lin, Zhengyuan Yang, Hanwang Zhang, Zicheng Liu, Lijuan Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generative AI has made significant strides in computer vision particularly in text-driven image/video synthesis (T2I/T2V). Despite the notable advancements it remains challenging in human-centric content synthesis such as realistic dance generation. Current methodologies primarily tailored for human motion transfer encounter difficulties when confronted with real-world dance scenarios (e.g. social media dance) which require to generalize across a wide spectrum of poses and intricate human details. In this paper we depart from the traditional paradigm of human motion transfer and emphasize two additional critical attributes for the synthesis of human dance content in social media contexts: (i) Generalizability: the model should be able to generalize beyond generic human viewpoints as well as unseen human subjects backgrounds and poses; (ii) Compositionality: it should allow for the seamless composition of seen/unseen subjects backgrounds and poses from different sources. To address these challenges we introduce DISCO which includes a novel model architecture with disentangled control to improve the compositionality of dance synthesis and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions. Code is available at https://disco-dance.github.io/.
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
Authors: Yuyin Zhou, Xianhang Li, Fengze Liu, Qingyue Wei, Xuxi Chen, Lequan Yu, Cihang Xie, Matthew P. Lungren, Lei Xing
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep neural networks have shown great success in representation learning. Deep neural networks have shown great success in representation learning. However when learning with noisy labels (LNL) they can easily overfit and fail to generalize to new data. This paper introduces a simple and effective method named Learning to Bootstrap (L2B) which enables models to bootstrap themselves using their own predictions without being adversely affected by erroneous pseudo-labels. It achieves this by dynamically adjusting the importance weight between real observed and generated labels as well as between different samples through meta-learning. Unlike existing instance reweighting methods the key to our method lies in a new versatile objective that enables implicit relabeling concurrently leading to significant improvements without incurring additional costs. L2B offers several benefits over the baseline methods. It yields more robust models that are less susceptible to the impact of noisy labels by guiding the bootstrapping procedure more effectively. It better exploits the valuable information contained in corrupted instances by adapting the weights of both instances and labels. Furthermore L2B is compatible with existing LNL methods and delivers competitive results spanning natural and medical imaging tasks including classification and segmentation under both synthetic and real-world noise. Extensive experiments demonstrate that our method effectively mitigates the challenges of noisy labels often necessitating few to no validation samples and is well generalized to other tasks such as image segmentation. This not only positions it as a robust complement to existing LNL techniques but also underscores its practical applicability. The code and models are available at https://github.com/yuyinzhou/l2b.
GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces
Authors: Yingwenqi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xiaoxiao Long, Wenping Wang, Yuexin Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The advent of neural 3D Gaussians has recently brought about a revolution in the field of neural rendering facilitating the generation of high-quality renderings at real-time speeds. However the explicit and discrete representation encounters challenges when applied to scenes featuring reflective surfaces. In this paper we present GaussianShader a novel method that applies a simplified shading function on 3D Gaussians to enhance the neural rendering in scenes with reflective surfaces while preserving the training and rendering efficiency. The main challenge in applying the shading function lies in the accurate normal estimation on discrete 3D Gaussians. Specifically we proposed a novel normal estimation framework based on the shortest axis directions of 3D Gaussians with a delicately designed loss to make the consistency between the normals and the geometries of Gaussian spheres. Experiments show that GaussianShader strikes a commendable balance between efficiency and visual quality. Our method surpasses Gaussian Splatting in PSNR on specular object datasets exhibiting an improvement of 1.57dB. When compared to prior works handling reflective surfaces such as Ref-NeRF our optimization time is significantly accelerated (23h vs. 0.58h).
Tactile-Augmented Radiance Fields
Authors: Yiming Dou, Fengyu Yang, Yi Liu, Antonio Loquercio, Andrew Owens
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present a scene representation that brings vision and touch into a shared 3D space which we call a tactile-augmented radiance field. This representation capitalizes on two key insights: (i) ubiquitous vision-based touch sensors are built on perspective cameras and (ii) visually and structurally similar regions of a scene share the same tactile features. We use these insights to train a conditional diffusion model that provided with an RGB image and a depth map rendered from a neural radiance field generates its corresponding tactile "image". To train this diffusion model we collect the largest collection of spatially-aligned visual and tactile data. Through qualitative and quantitative experiments we demonstrate the accuracy of our cross-modal generative model and the utility of collected and rendered visual-tactile pairs across a range of downstream tasks. Project page: https://dou-yiming.github.io/TaRF
Intensity-Robust Autofocus for Spike Camera
Authors: Changqing Su, Zhiyuan Ye, Yongsheng Xiao, You Zhou, Zhen Cheng, Bo Xiong, Zhaofei Yu, Tiejun Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Spike cameras a novel neuromorphic visual sensor can capture full-time spatial information through spike stream offering ultra-high temporal resolution and an extensive dynamic range. Autofocus control (AC) plays a pivotal role in a camera to efficiently capture information in challenging real-world scenarios. Nevertheless due to disparities in data modality and information characteristics compared to frame stream and event stream the current lack of efficient AC methods has made it challenging for spike cameras to adapt to intricate real-world conditions. To address this challenge we introduce a spike-based autofocus framework that includes a spike-specific focus measure called spike dispersion (SD) which effectively mitigates the influence of variations in scene light intensity during the focusing process by leveraging the spike camera's ability to record full-time spatial light intensity. Additionally the framework integrates a fast search strategy called spike-based golden fast search (SGFS) allowing rapid focal positioning without the need for a complete focus range traversal. To validate the performance of our method we have collected a spike-based autofocus dataset (SAD) containing synthetic data and real-world data under varying scene brightness and motion scenarios. Experimental results on these datasets demonstrate that our method offers state-of-the-art accuracy and efficiency. Furthermore experiments with data captured under varying scene brightness levels illustrate the robustness of our method to changes in light intensity during the focusing process.