CVPR 2024 论文列表

Text-to-video generation aims to produce a video based on a given prompt. Recently several commercial video models have been able to generate plausible videos with minimal noise excellent details and high aesthetic scores. However these models rely on large-scale well-filtered high-quality videos that are not accessible to the community. Many existing research works which train models using the low-quality WebVid-10M dataset struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method particularly in picture quality motion and concept composition.

Video-P2P is the first framework for real-world video editing with cross-attention control. While attention control has proven effective for image editing with pre-trained image generation models there are currently no large-scale video generation models publicly available. Video-P2P addresses this limitation by adapting an image generation diffusion model to complete various video editing tasks. Specifically we propose to first tune a Text-to-Set (T2S) model to complete an approximate inversion and then optimize a shared unconditional embedding to achieve accurate video inversion with a small memory cost. We further prove that it is crucial for consistent video editing. For attention control we introduce a novel decoupled-guidance strategy which uses different guidance strategies for the source and target prompts. The optimized unconditional embedding for the source prompt improves reconstruction ability while an initialized unconditional embedding for the target prompt enhances editability. Incorporating the attention maps of these two branches enables detailed editing. These technical designs enable various text-driven editing applications including word swap prompt refinement and attention re-weighting. Video-P2P works well on real-world videos for generating new characters while optimally preserving their original poses and scenes. It significantly outperforms previous approaches.

Diffusion models have made significant strides in image generation mastering tasks such as unconditional image synthesis text-image translation and image-to-image conversions. However their capability falls short in the realm of video prediction mainly because they treat videos as a collection of independent images relying on external constraints such as temporal attention mechanisms to enforce temporal coherence. In our paper we introduce a novel model class that treats video as a continuous multi-dimensional process rather than a series of discrete frames. Through extensive experimentation we establish state-of-the-art performance in video prediction validated on benchmark datasets including KTH BAIR Human3.6M and UCF101.

We present VIDIM a generative model for video interpolation which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data VIDIM uses cascaded diffusion models to first generate the target video at low resolution and then generate the high-resolution video conditioned on the low-resolution generated video. We compare VIDIM to previous state-of-the-art methods on video interpolation and demonstrate how such works fail in most settings where the underlying motion is complex nonlinear or ambiguous while VIDIM can easily handle such cases. We additionally demonstrate how classifier-free guidance on the start and end frame and conditioning the superresolution model on the original high-resolution frames without additional parameters unlocks high-fidelity results. VIDIM is fast to sample from as it jointly denoises all the frames to be generated requires less than a billion parameters per diffusion model to produce compelling results and still enjoys scalability and improved quality at larger parameter counts. Please see our project page at vidiminterpolation.github.io.

Diffusion models have made significant advances in generating high-quality images but their application to video generation has remained challenging due to the complexity of temporal motion. Zero-shot video editing offers a solution by utilizing pre-trained image diffusion models to translate source videos into new ones. Nevertheless existing methods struggle to maintain strict temporal consistency and efficient memory consumption. In this work we propose a novel approach to enhance temporal consistency in generated videos by merging self-attention tokens across frames. By aligning and compressing temporally redundant tokens across frames our method improves temporal coherence and reduces memory consumption in self-attention computations. The merging strategy matches and aligns tokens according to the temporal correspondence between frames facilitating natural temporal consistency in generated video frames. To manage the complexity of video processing we divide videos into chunks and develop intra-chunk local token merging and inter-chunk global token merging ensuring both short-term video continuity and long-term content consistency. Our video editing approach seamlessly extends the advancements in image editing to video editing rendering favorable results in temporal consistency over state-of-the-art methods.

Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality spatially consistent new views. While recent methods for view synthesis based on diffusion have shown great progress achieving consistency among various view estimates and at the same time abiding by the desired camera pose remains a critical problem yet to be solved. In this work we demonstrate a strikingly simple method where we utilize a pre-trained video diffusion model to solve this problem. Our key idea is that synthesizing a novel view could be reformulated as synthesizing a video of a camera going around the object of interest---a scanning video---which then allows us to leverage the powerful priors that a video diffusion model would have learned. Thus to perform novel-view synthesis we create a smooth camera trajectory to the target view that we wish to render and denoise using both a view-conditioned diffusion model and a video diffusion model. By doing so we obtain a highly consistent novel view synthesis outperforming the state of the art.

Route planning for navigation under partial observability plays a crucial role in modern robotics and autonomous driving. Existing route planning approaches can be categorized into two main classes: traditional autoregressive and diffusion-based methods. The former often fails due to its myopic nature while the latter either assumes full observability or struggles to adapt to unfamiliar scenarios due to strong couplings with behavior cloning from experts. To address these deficiencies we propose a versatile diffusion-based approach for both 2D and 3D route planning under partial observability. Specifically our value-guided diffusion policy first generates plans to predict actions across various timesteps providing ample foresight to the planning. It then employs a differentiable planner with state estimations to derive a value function directing the agent's exploration and goal-seeking behaviors without seeking experts while explicitly addressing partial observability. During inference our policy is further enhanced by a best-plan-selection strategy substantially boosting the planning success rate. Moreover we propose projecting point clouds derived from RGB-D inputs onto 2D grid-based bird-eye-view maps via semantic segmentation generalizing to 3D environments. This simple yet effective adaption enables zero-shot transfer from 2D-trained policy to 3D cutting across the laborious training for 3D policy and thus certifying our versatility. Experimental results demonstrate our superior performance particularly in navigating situations beyond expert demonstrations surpassing state-of-the-art autoregressive and diffusion-based baselines for both 2D and 3D scenarios.

We present VecFusion a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raster diffusion model followed by a vector diffusion model. The raster model generates low-resolution rasterized fonts with auxiliary control point information capturing the global style and shape of the font while the vector model synthesizes vector fonts conditioned on the low-resolution raster fonts from the first stage. To synthesize long and complex curves our vector diffusion model uses a transformer architecture and a novel vector representation that enables the modeling of diverse vector geometry and the precise prediction of control points. Our experiments show that in contrast to previous generative models for vector graphics our new cascaded vector diffusion model generates higher quality vector fonts with complex structures and diverse styles.

Recent innovations on text-to-3D generation have featured Score Distillation Sampling (SDS) which enables the zero-shot learning of implicit 3D models (NeRF) by directly distilling prior knowledge from 2D diffusion models. However current SDS-based models still struggle with intricate text prompts and commonly result in distorted 3D models with unrealistic textures or cross-view inconsistency issues. In this work we introduce a novel Visual Prompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the visual appearance knowledge in 2D visual prompt to boost text-to-3D generation. Instead of solely supervising SDS with text prompt VP3D first capitalizes on 2D diffusion model to generate a high-quality image from input text which subsequently acts as visual prompt to strengthen SDS optimization with explicit visual appearance. Meanwhile we couple the SDS optimization with additional differentiable reward function that encourages rendering images of 3D models to better visually align with 2D visual prompt and semantically match with text prompt. Through extensive experiments we show that the 2D Visual Prompt in our VP3D significantly eases the learning of visual appearance of 3D models and thus leads to higher visual fidelity with more detailed textures. It is also appealing in view that when replacing the self-generating visual prompt with a given reference image VP3D is able to trigger a new task of stylized text-to-3D generation. Our project page is available at https://vp3d-cvpr24.github.io.

Text-to-video diffusion models have advanced video generation significantly. However customizing these models to generate videos with tailored motions presents a substantial challenge. In specific they encounter hurdles in (1) accurately reproducing motion from a target video and (2) creating diverse visual variations. For example straightforward extensions of static image customization methods to video often lead to intricate entanglements of appearance and motion data. To tackle this here we present the Video Motion Customization (VMC) framework a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models. Our approach introduces a novel motion distillation objective using residual vectors between consecutive noisy latent frames as a motion reference. The diffusion process then preserve low-frequency motion trajectories while mitigating high-frequency motion-unrelated noise in image space. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts. Our codes and data can be found at: https://video-motion-customization.github.io/

Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models. Previous methods start by training a reward model that aligns with human preferences then leverage RL techniques to fine-tune the underlying models. However crafting an efficient reward model demands extensive datasets optimal architecture and manual hyperparameter tuning making the process both time and cost-intensive. The direct preference optimization (DPO) method effective in fine-tuning large language models eliminates the necessity for a reward model. However the extensive GPU memory requirement of the diffusion model's denoising process hinders the direct application of the DPO method. To address this issue we introduce the Direct Preference for Denoising Diffusion Policy Optimization (D3PO) method to directly fine-tune diffusion models. The theoretical analysis demonstrates that although D3PO omits training a reward model it effectively functions as the optimal reward model trained using human feedback data to guide the learning process. This approach requires no training of a reward model proving to be more direct cost-effective and minimizing computational overhead. In experiments our method uses the relative scale of objectives as a proxy for human preference delivering comparable results to methods using ground-truth rewards. Moreover D3PO demonstrates the ability to reduce image distortion rates and generate safer images overcoming challenges lacking robust reward models. Our code is publicly available at https://github.com/yk7333/D3PO.

Text-based diffusion models have exhibited remarkable success in generation and editing showing great promise for enhancing visual content with their generative prior. However applying these models to video super-resolution remains challenging due to the high demands for output fidelity and temporal consistency which is complicated by the inherent randomness in diffusion models. Our study introduces Upscale-A-Video a text-guided latent diffusion framework for video upscaling. This framework ensures temporal coherence through two key mechanisms: locally it integrates temporal layers into U-Net and VAE-Decoder maintaining consistency within short sequences; globally without training a flow-guided recurrent latent propagation module is introduced to enhance overall video stability by propagating and fusing latent across the entire sequences. Thanks to the diffusion paradigm our model also offers greater flexibility by allowing text prompts to guide texture creation and adjustable noise levels to balance restoration and generation enabling a trade-off between fidelity and quality. Extensive experiments show that Upscale-A-Video surpasses existing methods in both synthetic and real-world benchmarks as well as in AI-generated videos showcasing impressive visual realism and temporal consistency.

Unsupervised learning of keypoints and landmarks has seen significant progress with the help of modern neural network architectures but performance is yet to match the supervised counterpart making their practicability questionable. We leverage the emergent knowledge within text-to-image diffusion models towards more robust unsupervised keypoints. Our core idea is to find text embeddings that would cause the generative model to consistently attend to compact regions in images (i.e. keypoints). To do so we simply optimize the text embedding such that the cross-attention maps within the denoising network are localized as Gaussians with small standard deviations. We validate our performance on multiple datasets: the CelebA CUB-200-2011 Tai-Chi-HD DeepFashion and Human3.6m datasets. We achieve significantly improved accuracy sometimes even outperforming supervised ones particularly for data that is non-aligned and less curated. Our code is publicly available at https://stablekeypoints.github.io/.

Hyperspectral images (HSIs) have extensive applications in various fields such as medicine agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a challenge due to narrow-band spectral filtering. Consequently the importance of HSI denoising is substantial especially for snapshot hyperspectral imaging technology. While most previous HSI denoising methods are supervised creating supervised training datasets for the diverse scenes hyperspectral cameras and scan parameters is impractical. In this work we present Diff-Unmix a self-supervised denoising method for HSI using diffusion denoising generative models. Specifically Diff-Unmix addresses the challenge of recovering noise-degraded HSI through a fusion of Spectral Unmixing and conditional abundance generation. Firstly it employs a learnable block-based spectral unmixing strategy complemented by a pure transformer-based backbone. Then we introduce a self-supervised generative diffusion network to enhance abundance maps from the spectral unmixing block. This network reconstructs noise-free Unmixing probability distributions effectively mitigating noise-induced degradations within these components. Finally the reconstructed HSI is reconstructed through unmixing reconstruction by blending the diffusion-adjusted abundance map with the spectral endmembers. Experimental results on both simulated and real-world noisy datasets show that Diff-Unmix achieves state-of-the-art performance.

Semantic image synthesis i.e. generating images from user-provided semantic label maps is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling the iterative nature of their inference process makes them computationally demanding. Other approaches such as GANs are more efficient as they only need a single feed-forward pass for generation but the image quality tends to suffer when modeling large and diverse datasets. In this work we propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images by exploiting feature backbones pre-trained for tasks such as image classification. We also introduce a new generator architecture with better context modeling and using cross-attention to inject noise into latent variables leading to more diverse generated images. Our model which we dub DP-SIMS achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K COCO-Stuff and Cityscapes surpassing recent diffusion models while requiring two orders of magnitude less compute for inference.

We delve into Open Domain Generalization (ODG) marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to ODG face limitations due to constrained generalizations of traditional CNN backbones and errors in detecting target open samples in the absence of prior knowledge. Addressing these pitfalls we introduce ODG-CLIP harnessing the semantic prowess of the vision-language model CLIP. Our framework brings forth three primary innovations: Firstly distinct from prevailing paradigms we conceptualize ODG as a multi-class classification challenge encompassing both known and novel categories. Central to our approach is modeling a unique prompt tailored for detecting unknown class samples and to train this we employ a readily accessible stable diffusion model elegantly generating proxy images for the open class. Secondly aiming for domain-tailored classification (prompt) weights while ensuring a balance of precision and simplicity we devise a novel visual style-centric prompt learning mechanism. Finally we infuse images with class-discriminative knowledge derived from the prompt space to augment the fidelity of CLIP's visual embeddings. We introduce a novel objective to safeguard the continuity of this infused semantic intel across domains especially for the shared classes. Through rigorous testing on diverse datasets covering closed and open-set DG contexts ODG-CLIP demonstrates clear supremacy consistently outpacing peers with performance boosts between 8%-16%. Code will be available at https://github.com/mainaksingha01/ODG-CLIP.

This paper introduces a novel unified representation of diffusion models for image generation and segmentation. Specifically we use a colormap to represent entity-level masks addressing the challenge of varying entity numbers while aligning the representation closely with the image RGB domain. Two novel modules including the location-aware color palette and progressive dichotomy module are proposed to support our mask representation. On the one hand a location-aware palette guarantees the colors' consistency to entities' locations. On the other hand the progressive dichotomy module can efficiently decode the synthesized colormap to high-quality entity-level masks in a depth-first binary search without knowing the cluster numbers. To tackle the issue of lacking large-scale segmentation training data we employ an inpainting pipeline and then improve the flexibility of diffusion models across various tasks including inpainting image synthesis referring segmentation and entity segmentation. Comprehensive experiments validate the efficiency of our approach demonstrating comparable segmentation mask quality to state-of-the-art and adaptability to multiple tasks.

Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information. Therefore we propose UnSAMFlow an unsupervised flow network that also leverages object information from the latest foundation model Segment Anything Model (SAM). We first include a self-supervised semantic augmentation module tailored to SAM masks. We also analyze the poor gradient landscapes of traditional smoothness losses and propose a new smoothness definition based on homography instead. A simple yet effective mask feature module has also been added to further aggregate features on the object level. With all these adaptations our method produces clear optical flow estimation with sharp boundaries around objects which outperforms state-of-the-art methods on both KITTI and Sintel datasets. Our method also generalizes well across domains and runs very efficiently.