CVPR 2024 论文列表

Advances in camera-based physiological monitoring have enabled the robust non-contact measurement of respiration and the cardiac pulse which are known to be indicative of the sleep stage. This has led to research into camera-based sleep monitoring as a promising alternative to "gold-standard" polysomnography which is cumbersome expensive to administer and hence unsuitable for longer-term clinical studies. In this paper we introduce SleepVST a transformer model which enables state-of-the-art performance in camera-based sleep stage classification (sleep staging). After pre-training on contact sensor data SleepVST outperforms existing methods for cardio-respiratory sleep staging on the SHHS and MESA datasets achieving total Cohen's kappa scores of 0.75 and 0.77 respectively. We then show that SleepVST can be successfully transferred to cardio-respiratory waveforms extracted from video enabling fully contact-free sleep staging. Using a video dataset of 50 nights we achieve a total accuracy of 78.8% and a Cohen's \kappa of 0.71 in four-class video-based sleep staging setting a new state-of-the-art in the domain.

The recent wave of AI-generated content has witnessed the great development and success of Text-to-Image (T2I) technologies. By contrast Text-to-Video (T2V) still falls short of expectations though attracting increasing interest. Existing works either train from scratch or adapt large T2I model to videos both of which are computation and resource expensive. In this work we propose a Simple Diffusion Adapter (SimDA) that fine-tunes only 24M out of 1.1B parameters of a strong T2I model adapting it to video generation in a parameter-efficient way. In particular we turn the T2I model for T2V by designing light-weight spatial and temporal adapters for transfer learning. Besides we change the original spatial attention to the proposed Latent-Shift Attention (LSA) for temporal consistency. With a similar model architecture we further train a video super-resolution model to generate high-definition (1024 x 1024) videos. In addition to T2V generation in the wild SimDA could also be utilized in one-shot video editing with only 2 minutes tuning. Doing so our method could minimize the training effort with extremely few tunable parameters for model adaptation.

Diffusion-based models have gained significant popularity for text-to-image generation due to their exceptional image-generation capabilities. A risk with these models is the potential generation of inappropriate content such as biased or harmful images. However the underlying reasons for generating such undesired content from the perspective of the diffusion model's internal representation remain unclear. Previous work interprets vectors in an interpretable latent space of diffusion models as semantic concepts. However existing approaches cannot discover directions for arbitrary concepts such as those related to inappropriate concepts. In this work we propose a novel self-supervised approach to find interpretable latent directions for a given concept. With the discovered vectors we further propose a simple approach to mitigate inappropriate generation. Extensive experiments have been conducted to verify the effectiveness of our mitigation approach namely for fair generation safe generation and responsible text-enhancing generation. Project page: https://interpretdiffusion.github.io.

In text-to-image personalization a timely and crucial challenge is the tendency of generated images overfitting to the biases present in the reference images. We initiate our study with a comprehensive categorization of the biases into background nearby-object tied-object substance (in style re-contextualization) and pose biases. These biases manifest in the generated images due to their entanglement into the subject embedding. This undesired embedding entanglement not only results in the reflection of biases from the reference images into the generated images but also notably diminishes the alignment of the generated images with the given generation prompt. To address this challenge we propose SID (Selectively Informative Description) a text description strategy that deviates from the prevalent approach of only characterizing the subject's class identification. SID is generated utilizing multimodal GPT-4 and can be seamlessly integrated into optimization-based models. We present comprehensive experimental results along with analyses of cross-attention maps subject-alignment non-subject-disentanglement and text-alignment.

Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g. faces on the back view) and inaccurate shapes (e.g. animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure multi-view consistency while struggling to balance 2D generation quality with 3D consistency. In this paper we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. Moreover to ensure accurate appearances of different views we further modulate the output of the 2D diffusion model to the correct patterns of the template views without altering the generated object's style. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model. Extensive experiments show our method can largely improve the multi-view consistency while retaining fidelity and diversity.

Long-form video content constitutes a significant portion of internet traffic making automated video summarization an essential research problem. However existing video summarization datasets are notably limited in their size constraining the effectiveness of state-of-the-art methods for generalization. Our work aims to overcome this limitation by capitalizing on the abundance of long-form videos with dense speech-to-video alignment and the remarkable capabilities of recent large language models (LLMs) in summarizing long text. We introduce an automated and scalable pipeline for generating a large-scale video summarization dataset using LLMs as Oracle summarizers. By leveraging the generated dataset we analyze the limitations of existing approaches and propose a new video summarization model that effectively addresses them. To facilitate further research in the field our work also presents a new benchmark dataset that contains 1200 long videos each with high-quality summaries annotated by professionals. Extensive experiments clearly indicate that our proposed approach sets a new state-of-the-art in video summarization across several benchmarks.

Recently text-guided scalable vector graphics (SVGs) synthesis has shown promise in domains such as iconography and sketch. However existing text-to-SVG generation methods lack editability and struggle with visual quality and result diversity. To address these limitations we propose a novel text-guided vector graphics synthesis method called SVGDreamer. SVGDreamer incorporates a semantic-driven image vectorization (SIVE) process that enables the decomposition of synthesis into foreground objects and background thereby enhancing editability. Specifically the SIVE process introduces attention-based primitive control and an attention-mask loss function for effective control and manipulation of individual elements. Additionally we propose a Vectorized Particle-based Score Distillation (VPSD) approach to address issues of shape over-smoothing color over-saturation limited diversity and slow convergence of the existing text-to-SVG generation methods by modeling SVGs as distributions of control points and colors. Furthermore VPSD leverages a reward model to re-weight vector particles which improves aesthetic appeal and accelerates convergence. Extensive experiments are conducted to validate the effectiveness of SVGDreamer demonstrating its superiority over baseline methods in terms of editability visual quality and diversity. Project page: \href https://ximinng.github.io/SVGDreamer-project/ https://ximinng.github.io/SVGDreamer-project/

Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. However many generated images still suffer from issues such as artifacts/implausibility misalignment with text descriptions and low aesthetic quality. Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) for large language models prior works collected human-provided scores as feedback on generated images and trained a reward model to improve the T2I generation. In this paper we enrich the feedback signal by (i) marking image regions that are implausible or misaligned with the text and (ii) annotating which words in the text prompt are misrepresented or missing on the image. We collect such rich human feedback on 18K generated images (RichHF-18K) and train a multimodal transformer to predict the rich feedback automatically. We show that the predicted rich human feedback can be leveraged to improve image generation for example by selecting high-quality training data to finetune and improve the generative models or by creating masks with predicted heatmaps to inpaint the problematic regions. Notably the improvements generalize to models (Muse) beyond those used to generate the images on which human feedback data were collected (Stable Diffusion variants). The RichHF-18K data set will be released in our GitHub repository: https://github.com/google-research/google-research/tree/master/richhf_18k.

In this paper we address the challenge of reconstructing general articulated 3D objects from a single video. Existing works employing dynamic neural radiance fields have advanced the modeling of articulated objects like humans and animals from videos but face challenges with piece-wise rigid general articulated objects due to limitations in their deformation models. To tackle this we propose Quasi-Rigid Blend Skinning a novel deformation model that enhances the rigidity of each part while maintaining flexible deformation of the joints. Our primary insight combines three distinct approaches: 1) an enhanced bone rigging system for improved component modeling 2) the use of quasi-sparse skinning weights to boost part rigidity and reconstruction fidelity and 3) the application of geodesic point assignment for precise motion and seamless deformation. Our method outperforms previous works in producing higher-fidelity 3D reconstructions of general articulated objects as demonstrated on both real and synthetic datasets. Project page: https://chaoyuesong.github.io/REACTO.

Current state-of-the-art Video Object Segmentation (VOS) methods rely on dense per-object mask annotations both during training and testing. This requires time-consuming and costly video annotation mechanisms. We propose a novel Point-VOS task with a spatio-temporally sparse point-wise annotation scheme that substantially reduces the annotation effort. We apply our annotation scheme to two large-scale video datasets with text descriptions and annotate over 19M points across 133K objects in 32K videos. Based on our annotations we propose a new Point-VOS benchmark and a corresponding point-based training mechanism which we use to establish strong baseline results. We show that existing VOS methods can easily be adapted to leverage our point annotations during training and can achieve results close to the fully-supervised performance when trained on pseudo-masks generated from these points. In addition we show that our data can be used to improve models that connect vision and language by evaluating it on the Video Narrative Grounding (VNG) task. We will make our code and annotations available at https://pointvos.github.io.

We present personalized residuals and localized attention-guided sampling for efficient concept-driven generation using text-to-image diffusion models. Our method first represents concepts by freezing the weights of a pretrained text-conditioned diffusion model and learning low-rank residuals for a small subset of the model's layers. The residual-based approach then directly enables application of our proposed sampling technique which applies the learned residuals only in areas where the concept is localized via cross-attention and applies the original diffusion weights in all other regions. Localized sampling therefore combines the learned identity of the concept with the existing generative prior of the underlying diffusion model. We show that personalized residuals effectively capture the identity of a concept in 3 minutes on a single GPU without the use of regularization images and with fewer parameters than previous models and localized sampling allows using the original model as strong prior for large parts of the image.

Previous methods for Video Frame Interpolation (VFI) have encountered challenges notably the manifestation of blur and ghosting effects. These issues can be traced back to two pivotal factors: unavoidable motion errors and misalignment in supervision. In practice motion estimates often prove to be error-prone resulting in misaligned features. Furthermore the reconstruction loss tends to bring blurry results particularly in misaligned regions. To mitigate these challenges we propose a new paradigm called PerVFI (Perception-oriented Video Frame Interpolation). Our approach incorporates an Asymmetric Synergistic Blending module (ASB) that utilizes features from both sides to synergistically blend intermediate features. One reference frame emphasizes primary content while the other contributes complementary information. To impose a stringent constraint on the blending process we introduce a self-learned sparse quasi-binary mask which effectively mitigates ghosting and blur artifacts in the output. Additionally we employ a normalizing flow-based generator and utilize the negative log-likelihood loss to learn the conditional distribution of the output which further facilitates the generation of clear and fine details. Experimental results validate the superiority of PerVFI demonstrating significant improvements in perceptual quality compared to existing methods. Codes are available at https://github.com/mulns/PerVFI

Video quality assessment (VQA) is a challenging problem due to the numerous factors that can affect the perceptual quality of a video e.g. content attractiveness distortion type motion pattern and level. However annotating the Mean opinion score (MOS) for videos is expensive and time-consuming which limits the scale of VQA datasets and poses a significant obstacle for deep learning-based methods. In this paper we propose a VQA method named PTM-VQA which leverages PreTrained Models to transfer knowledge from models pretrained on various pre-tasks enabling benefits for VQA from different aspects. Specifically we extract features of videos from different pretrained models with frozen weights and integrate them to generate representation. Since these models possess various fields of knowledge and are often trained with labels irrelevant to quality we propose an Intra-Consistency and Inter-Divisibility (ICID) loss to impose constraints on features extracted by multiple pretrained models. The intra-consistency constraint ensures that features extracted by different pretrained models are in the same unified quality-aware latent space while the inter-divisibility introduces pseudo clusters based on the annotation of samples and tries to separate features of samples from different clusters. Furthermore with a constantly growing number of pretrained models it is crucial to determine which models to use and how to use them. To address this problem we propose an efficient scheme to select suitable candidates. Models with better clustering performance on VQA datasets are chosen to be our candidates. Extensive experiments demonstrate the effectiveness of the proposed method.

Diffusion models trained on large-scale text-image datasets have demonstrated a strong capability of controllable high-quality image generation from arbitrary text prompts. However the generation quality and generalization ability of 3D diffusion models is hindered by the scarcity of high-quality and large-scale 3D datasets. In this paper we present PI3D a framework that fully leverages the pre-trained text-to-image diffusion models' ability to generate high-quality 3D shapes from text prompts in minutes. The core idea is to connect the 2D and 3D domains by representing a 3D shape as a set of Pseudo RGB Images. We fine-tune an existing text-to-image diffusion model to produce such pseudo-images using a small number of text-3D pairs. Surprisingly we find that it can already generate meaningful and consistent 3D shapes given complex text descriptions. We further take the generated shapes as the starting point for a lightweight iterative refinement using score distillation sampling to achieve high-quality generation under a low budget. PI3D generates a single 3D shape from text in only 3 minutes and the quality is validated to outperform existing 3D generative models by a large margin.

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However a significant limitation is their inability to offer interactive control to users a feature that promises to open up unprecedented applications and creativity. In this work we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present Peekaboo a novel masked attention module which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that Peekaboo achieves up to a 3.8x improvement in mIoU over baseline models all while maintaining the same latency. Code and benchmark are available on the webpage.

Scaling up model and data size has been quite successful for the evolution of LLMs. However the scaling law for the diffusion based text-to-image (T2I) models is not fully explored. It is also unclear how to efficiently scale the model for better performance at reduced cost. The different training settings and expensive training cost make a fair model comparison extremely difficult. In this work we empirically study the scaling properties of diffusion based T2I models by performing extensive and rigours ablations on scaling both denoising backbones and training set including training scaled UNet and Transformer variants ranging from 0.4B to 4B parameters on datasets upto 600M images. For model scaling we find the location and amount of cross attention distinguishes the performance of existing UNet designs. And increasing the transformer blocks is more parameter-efficient for improving text-image alignment than increasing channel numbers. We then identify an efficient UNet variant which is 45% smaller and 28% faster than SDXL's UNet. On the data scaling side we show the quality and diversity of the training set matters more than simply dataset size. Increasing caption density and diversity improves text-image alignment performance and the learning efficiency. Finally we provide scaling functions to predict the text-image alignment performance as functions of the scale of model size compute and dataset size.

Frechet Video Distance (FVD) a prominent metric for evaluating video generation models is known to conflict with human perception occasionally. In this paper we aim to explore the extent of FVD's bias toward frame quality over temporal realism and identify its sources. We first quantify the FVD's sensitivity to the temporal axis by decoupling the frame and motion quality and find that the FVD only increases slightly with larger temporal corruption. We then analyze the generated videos and show that via careful sampling from a large set of generated videos that do not contain motions one can drastically decrease FVD without improving the temporal quality. Both studies suggest FVD's basis towards the quality of individual frames. We show that FVD with features extracted from the recent large-scale self-supervised video models is less biased toward image quality. Finally we revisit a few real-world examples to validate our hypothesis.

The core of video understanding tasks such as recognition captioning and tracking is to automatically detect objects or actions in a video and analyze their temporal evolution. Despite sharing a common goal different tasks often rely on distinct model architectures and annotation formats. In contrast natural language processing benefits from a unified output space i.e. text sequences which simplifies the training of powerful foundational language models such as GPT-3 with extensive training corpora. Inspired by this we seek to unify the output space of video understanding tasks by using languages as labels and additionally introducing time and box tokens. In this way a variety of video tasks could be formulated as video-grounded token generation. This enables us to address various types of video tasks including classification (such as action recognition) captioning (covering clip captioning video question answering and dense video captioning) and localization tasks (such as visual object tracking) within a fully shared encoder-decoder architecture following a generative framework. Through comprehensive experiments we demonstrate such a simple and straightforward idea is quite effective and can achieve state-of-the-art or competitive results on seven video benchmarks providing a novel perspective for more universal video understanding. Code is available at \href https://github.com/wangjk666/OmniVid https://github.com/wangjk666/OmniVid .