CVPR 2024 论文列表

Generative models have been very popular in the recent years for their image generation capabilities. GAN-based models are highly regarded for their disentangled latent space which is a key feature contributing to their success in controlled image editing. On the other hand diffusion models have emerged as powerful tools for generating high-quality images. However the latent space of diffusion models is not as thoroughly explored or understood. Existing methods that aim to explore the latent space of diffusion models usually relies on text prompts to pinpoint specific semantics. However this approach may be restrictive in areas such as art fashion or specialized fields like medicine where suitable text prompts might not be available or easy to conceive thus limiting the scope of existing work. In this paper we propose an unsupervised method to discover latent semantics in text-to-image diffusion models without relying on text prompts. Our method takes a small set of unlabeled images from specific domains such as faces or cats and a pre-trained diffusion model and discovers diverse semantics in unsupervised fashion using a contrastive learning objective. Moreover the learned directions can be applied simultaneously either within the same domain (such as various types of facial edits) or across different domains (such as applying cat and face edits within the same image) without interfering with each other. Our extensive experiments show that our method achieves highly disentangled edits outperforming existing approaches in both diffusion-based and GAN-based latent space editing methods.

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.

Controllable generation of 3D assets is important for many practical applications like content creation in movies games and engineering as well as in AR/VR. Recently diffusion models have shown remarkable results in generation quality of 3D objects. However none of the existing models enable disentangled generation to control the shape and appearance separately. For the first time we present a suitable representation for 3D diffusion models to enable such disentanglement by introducing a hybrid point cloud and neural radiance field approach. We model a diffusion process over point positions jointly with a high-dimensional feature space for a local density and radiance decoder. While the point positions represent the coarse shape of the object the point features allow modeling the geometry and appearance details. This disentanglement enables us to sample both independently and therefore to control both separately. Our approach sets a new state of the art in generation compared to previous disentanglement-capable methods by reduced FID scores of 30-90% and is on-par with other non-disentanglement-capable state-of-the art methods.

Stereo matching is a core task for many computer vision and robotics applications. Despite their dominance in traditional stereo methods the hand-crafted Markov Random Field (MRF) models lack sufficient modeling accuracy compared to end-to-end deep models. While deep learning representations have greatly improved the unary terms of the MRF models the overall accuracy is still severely limited by the hand-crafted pairwise terms and message passing. To address these issues we propose a neural MRF model where both potential functions and message passing are designed using data-driven neural networks. Our fully data-driven model is built on the foundation of variational inference theory to prevent convergence issues and retain stereo MRF's graph inductive bias. To make the inference tractable and scale well to high-resolution images we also propose a Disparity Proposal Network (DPN) to adaptively prune the search space of disparity. The proposed approach ranks 1^ st on both KITTI 2012 and 2015 leaderboards among all published methods while running faster than 100 ms. This approach significantly outperforms prior global methods e.g. lowering D1 metric by more than 50% on KITTI 2015. In addition our method exhibits strong cross-domain generalization and can recover sharp edges. The codes at https://github.com/aeolusguan/NMRF.

We propose NeRFiller an approach that completes missing portions of a 3D capture via generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a captured 3D scene or object are missing due to mesh reconstruction failures or a lack of observations (e.g. contact regions such as the bottom of objects or hard-to-reach areas). We approach this challenging 3D inpainting problem by leveraging a 2D inpainting diffusion model. We identify a surprising behavior of these models where they generate more 3D consistent inpaints when images form a 2x2 grid and show how to generalize this behavior to more than four images. We then present an iterative framework to distill these inpainted regions into a single consistent 3D scene. In contrast to related works we focus on completing scenes rather than deleting foreground objects and our approach does not require tight 2D object masks or text. We compare our approach to relevant baselines adapted to our setting on a variety of scenes where NeRFiller creates the most 3D consistent and plausible scene completions. Our project page is at https://ethanweber.me/nerfiller/.

Faithfully modeling the space of articulations is a crucial task that allows recovery and generation of realistic poses and remains a notorious challenge. To this end we introduce Neural Riemannian Distance Fields (NRDFs) data-driven priors modeling the space of plausible articulations represented as the zero-level-set of a neural field in a high-dimensional product-quaternion space. To train NRDFs only on positive examples we introduce a new sampling algorithm ensuring that the geodesic distances follow a desired distribution yielding a principled distance field learning paradigm. We then devise a projection algorithm to map any random pose onto the level-set by an adaptive-step Riemannian optimizer adhering to the product manifold of joint rotations at all times. NRDFs can compute the Riemannian gradient via backpropagation and by mathematical analogy are related to Riemannian flow matching a recent generative model. We conduct a comprehensive evaluation of NRDF against other pose priors in various downstream tasks i.e. pose generation image-based pose estimation and solving inverse kinematics highlighting NRDF's superior performance. Besides humans NRDF's versatility extends to hand and animal poses as it can effectively represent any articulation.

The success of denoising diffusion models in representing rich data distributions over 2D raster images has prompted research on extending them to other data representations such as vector graphics. Unfortunately due to their variable structure and scarcity of vector training data directly applying diffusion models on this domain remains a challenging problem. Using workarounds like optimization via Score Distillation Sampling (SDS) is also fraught with difficulty as vector representations are non-trivial to directly optimize and tend to result in implausible geometries such as redundant or self-intersecting shapes. NIVeL addresses these challenges by reinterpreting the problem on an alternative intermediate domain which preserves the desirable properties of vector graphics - mainly sparsity of representation and resolution-independence. This alternative domain is based on neural implicit fields expressed in a set of decomposable editable layers. Based on our experiments NIVeL produces text-to-vector graphics results of significantly better quality than the state-of-the-art.

We address the problem of generating realistic 3D motions of humans interacting with objects in a scene. Our key idea is to create a neural interaction field attached to a specific object which outputs the distance to the valid interaction manifold given a human pose as input. This interaction field guides the sampling of an object-conditioned human motion diffusion model so as to encourage plausible contacts and affordance semantics. To support interactions with scarcely available data we propose an automated synthetic data pipeline. For this we seed a pre-trained motion model which has priors for the basics of human movement with interaction-specific anchor poses extracted from limited motion capture data. Using our guided diffusion model trained on generated synthetic data we synthesize realistic motions for sitting and lifting with several objects outperforming alternative approaches in terms of motion quality and successful action completion. We call our framework NIFTY: Neural Interaction Fields for Trajectory sYnthesis.

We introduce MultiDiff a novel approach for consistent novel view synthesis of scenes from a single RGB image. The task of synthesizing novel views from a single reference image is highly ill-posed by nature as there exist multiple plausible explanations for unobserved areas. To address this issue we incorporate strong priors in form of monocular depth predictors and video-diffusion models. Monocular depth enables us to condition our model on warped reference images for the target views increasing geometric stability. The video-diffusion prior provides a strong proxy for 3D scenes allowing the model to learn continuous and pixel-accurate correspondences across generated images. In contrast to approaches relying on autoregressive image generation that are prone to drifts and error accumulation MultiDiff jointly synthesizes a sequence of frames yielding high-quality and multi-view consistent results -- even for long-term scene generation with large camera movements while reducing inference time by an order of magnitude. For additional consistency and image quality improvements we introduce a novel structured noise distribution. Our experimental results demonstrate that MultiDiff outperforms state-of-the-art methods on the challenging real-world datasets RealEstate10K and ScanNet. Finally our model naturally supports multi-view consistent editing without the need for further tuning.

Despite significant advancements in text-to-motion synthesis generating language-guided human motion within 3D environments poses substantial challenges. These challenges stem primarily from (i) the absence of powerful generative models capable of jointly modeling natural language 3D scenes and human motion and (ii) the generative models' intensive data requirements contrasted with the scarcity of comprehensive high-quality language-scene-motion datasets. To tackle these issues we introduce a novel two-stage framework that employs scene affordance as an intermediate representation effectively linking 3D scene grounding and conditional motion generation. Our framework comprises an Affordance Diffusion Model (ADM) for predicting explicit affordance map and an Affordance-to-Motion Diffusion Model (AMDM) for generating plausible human motions. By leveraging scene affordance maps our method overcomes the difficulty in generating human motion under multimodal condition signals especially when training with limited data lacking extensive language-scene-motion pairs. Our extensive experiments demonstrate that our approach consistently outperforms all baselines on established benchmarks including HumanML3D and HUMANISE. Additionally we validate our model's exceptional generalization capabilities on a specially curated evaluation set featuring previously unseen descriptions and scenes.

Diffusion models generate images with an unprecedented level of quality but how can we freely rearrange image layouts? Recent works generate controllable scenes via learning spatially disentangled latent codes but these methods do not apply to diffusion models due to their fixed forward process. In this work we propose SceneDiffusion to optimize a layered scene representation during the diffusion sampling process. Our key insight is that spatial disentanglement can be obtained by jointly denoising scene renderings at different spatial layouts. Our generated scenes support a wide range of spatial editing operations including moving resizing cloning and layer-wise appearance editing operations including object restyling and replacing. Moreover a scene can be generated conditioned on a reference image thus enabling object moving for in-the-wild images. Notably this approach is training-free compatible with general text-to-image diffusion models and responsive in less than a second.

Existing diffusion-based video editing models have made gorgeous advances for editing attributes of a source video over time but struggle to manipulate the motion information while preserving the original protagonist's appearance and background. To address this we propose MotionEditor the first diffusion model for video motion editing. MotionEditor incorporates a novel content-aware motion adapter into ControlNet to capture temporal motion correspondence. While ControlNet enables direct generation based on skeleton poses it encounters challenges when modifying the source motion in the inverted noise due to contradictory signals between the noise (source) and the condition (reference). Our adapter complements ControlNet by involving source content to transfer adapted control signals seamlessly. Further we build up a two-branch architecture (a reconstruction branch and an editing branch) with a high-fidelity attention injection mechanism facilitating branch interaction. This mechanism enables the editing branch to query the key and value from the reconstruction branch in a decoupled manner making the editing branch retain the original background and protagonist appearance. We also propose a skeleton alignment algorithm to address the discrepancies in pose size and position. Experiments demonstrate the promising motion editing ability of MotionEditor both qualitatively and quantitatively. To the best of our knowledge MotionEditor is the first to use diffusion models specifically for video motion editing considering the origin dynamic background and camera movement.

We introduce Motion2VecSets a 4D diffusion model for dynamic surface reconstruction from point cloud sequences. While existing state-of-the-art methods have demonstrated success in reconstructing non-rigid objects using neural field representations conventional feed-forward networks encounter challenges with ambiguous observations from noisy partial or sparse point clouds. To address these challenges we introduce a diffusion model that explicitly learns the shape and motion distribution of non-rigid objects through an iterative denoising process of compressed latent representations. The diffusion-based priors enable more plausible and probabilistic reconstructions when handling ambiguous inputs. We parameterize 4D dynamics with latent sets instead of using global latent codes. This novel 4D representation allows us to learn local shape and deformation patterns leading to more accurate non-linear motion capture and significantly improving generalizability to unseen motions and identities. For more temporally-coherent object tracking we synchronously denoise deformation latent sets and exchange information across multiple frames. To avoid computational overhead we designed an interleaved space and time attention block to alternately aggregate deformation latents along spatial and temporal domains. Extensive comparisons against state-of-the-art methods demonstrate the superiority of our Motion2VecSets in 4D reconstruction from various imperfect observations.

Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design choice is the shape representation. An effective shape representation needs to adhere three design principles: it should allow an efficient conversion of large 3D datasets to the representation form; it should provide a good tradeoff of approximation power versus number of parameters; and it should have a simple tensorial form that is compatible with existing powerful neural architectures. While standard 3D shape representations such as volumetric grids and point clouds do not adhere to all these principles simultaneously we advocate in this paper a new representation that does. We introduce Mosaic-SDF (M-SDF): a simple 3D shape representation that approximates the Signed Distance Function (SDF) of a given shape by using a set of local grids spread near the shape's boundary. The M-SDF representation is fast to compute for each shape individually making it readily parallelizable; it is parameter efficient as it only covers the space around the shape's boundary; and it has a simple matrix form compatible with Transformer-based architectures. We demonstrate the efficacy of the M-SDF representation by using it to train a 3D generative flow model including class-conditioned generation with the ShapeNetCore-V2 (3D Warehouse) dataset and text-to-3D generation using a dataset of about 600k caption-shape pairs.

Recent advances in generative diffusion models have enabled the previously unfeasible capability of generating 3D assets from a single input image or a text prompt. In this work we aim to enhance the quality and functionality of these models for the task of creating controllable photorealistic human avatars. We achieve this by integrating a 3D morphable model into the state-of-the-art multi-view-consistent diffusion approach. We demonstrate that accurate conditioning of a generative pipeline on the articulated 3D model enhances the baseline model performance on the task of novel view synthesis from a single image. More importantly this integration facilitates a seamless and accurate incorporation of facial expression and body pose control into the generation process. To the best of our knowledge our proposed framework is the first diffusion model to enable the creation of fully 3D-consistent animatable and photorealistic human avatars from a single image of an unseen subject; extensive quantitative and qualitative evaluations demonstrate the advantages of our approach over existing state-of-the-art avatar creation models on both novel view and novel expression synthesis tasks. The code for our project is publicly available.

3D object detection and pose estimation from a single-view image is challenging due to the high uncertainty caused by the absence of 3D perception. As a solution recent monocular 3D detection methods leverage additional modalities such as stereo image pairs and LiDAR point clouds to enhance image features at the expense of additional annotation costs. We propose using diffusion models to learn effective representations for monocular 3D detection without additional modalities or training data. We present MonoDiff a novel framework that employs the reverse diffusion process to estimate 3D bounding box and orientation. But considering the variability in bounding box sizes along different dimensions it is ineffective to sample noise from a standard Gaussian distribution. Hence we adopt a Gaussian mixture model to sample noise during the forward diffusion process and initialize the reverse diffusion process. Furthermore since the diffusion model generates the 3D parameters for a given object image we leverage 2D detection information to provide additional supervision by maintaining the correspondence between 3D/2D projection. Finally depending on the signal-to-noise ratio we incorporate a dynamic weighting scheme to account for the level of uncertainty in the supervision by projection at different timesteps. MonoDiff outperforms current state-of-the-art monocular 3D detection methods on the KITTI and Waymo benchmarks without additional depth priors. MonoDiff project is available at: https://dylran.github.io/monodiff.github.io.

We introduce MoMask a novel masked modeling framework for text-driven 3D human motion generation. In MoMask a hierarchical quantization scheme is employed to represent human motion as multi-layer discrete motion tokens with high-fidelity details. Starting at the base layer with a sequence of motion tokens obtained by vector quantization the residual tokens of increasing orders are derived and stored at the subsequent layers of the hierarchy. This is consequently followed by two distinct bidirectional transformers. For the base-layer motion tokens a Masked Transformer is designated to predict randomly masked motion tokens conditioned on text input at training stage. During generation (i.e. inference) stage starting from an empty sequence our Masked Transformer iteratively fills up the missing tokens; Subsequently a Residual Transformer learns to progressively predict the next-layer tokens based on the results from current layer. Extensive experiments demonstrate that MoMask outperforms the state-of-art methods on the text-to-motion generation task with an FID of 0.045 (vs e.g. 0.141 of T2M-GPT) on the HumanML3D dataset and 0.228 (vs 0.514) on KIT-ML respectively. MoMask can also be seamlessly applied in related tasks without further model fine-tuning such as text-guided temporal inpainting.

Deep neural networks (DNNs) are vulnerable to adversarial perturbation where an imperceptible perturbation is added to the image that can fool the DNNs. Diffusion-based adversarial purification uses the diffusion model to generate a clean image against such adversarial attacks. Unfortunately the generative process of the diffusion model is also inevitably affected by adversarial perturbation since the diffusion model is also a deep neural network where its input has adversarial perturbation. In this work we propose MimicDiffusion a new diffusion-based adversarial purification technique that directly approximates the generative process of the diffusion model with the clean image as input. Concretely we analyze the differences between the guided terms using the clean image and the adversarial sample. After that we first implement MimicDiffusion based on Manhattan distance. Then we propose two guidance to purify the adversarial perturbation and approximate the clean diffusion model. Extensive experiments on three image datasets including CIFAR-10 CIFAR-100 and ImageNet with three classifier backbones including WideResNet-70-16 WideResNet-28-10 and ResNet-50 demonstrate that MimicDiffusion significantly performs better than the state-of-the-art baselines. On CIFAR-10 CIFAR-100 and ImageNet it achieves 92.67% 61.35% and 61.53% average robust accuracy which are 18.49% 13.23% and 17.64% higher respectively. The code is available at https://github.com/psky1111/MimicDiffusion.

Volumetric optical microscopy using non-diffracting beams enables rapid imaging of 3D volumes by projecting them axially to 2D images but lacks crucial depth information. Addressing this we introduce MicroDiffusion a pioneering tool facilitating high-quality depth-resolved 3D volume reconstruction from limited 2D projections. While existing Implicit Neural Representation (INR) models often yield incomplete outputs and Denoising Diffusion Probabilistic Models (DDPM) excel at capturing details our method integrates INR's structural coherence with DDPM's fine-detail enhancement capabilities. We pretrain an INR model to transform 2D axially-projected images into a preliminary 3D volume. This pretrained INR acts as a global prior guiding DDPM's generative process through a linear interpolation between INR outputs and noise inputs. This strategy enriches the diffusion process with structured 3D information enhancing detail and reducing noise in localized 2D images.By conditioning the diffusion model on the closest 2D projection MicroDiffusion substantially enhances fidelity in resulting 3D reconstructions surpassing INR and standard DDPM outputs with unparalleled image quality and structural fidelity. Our code and dataset are available athttps://github.com/UCSC-VLAA/MicroDiffusion.