CVPR 2024 Papers

We introduce PhysGaussian a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis. Employing a customized Material Point Method (MPM) our approach enriches 3D Gaussian kernels with physically meaningful kinematic deformation and mechanical stress attributes all evolved in line with continuum mechanics principles. A defining characteristic of our method is the seamless integration between physical simulation and visual rendering: both components utilize the same 3D Gaussian kernels as their discrete representations. This negates the necessity for triangle/tetrahedron meshing marching cubes cage meshes or any other geometry embedding highlighting the principle of "what you see is what you simulate (WS^2)". Our method demonstrates exceptional versatility across a wide variety of materials--including elastic entities plastic metals non-Newtonian fluids and granular materials--showcasing its strong capabilities in creating diverse visual content with novel viewpoints and movements.

This paper presents Paint3D a novel coarse-to-fine generative framework that is capable of producing high-resolution lighting-less and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs. The key challenge addressed is generating high-quality textures without embedded illumination information which allows the textures to be re-lighted or re-edited within modern graphics pipelines. To achieve this our method first leverages a pre-trained depth-aware 2D diffusion model to generate view-conditional images and perform multi-view texture fusion producing an initial coarse texture map. However as 2D models cannot fully represent 3D shapes and disable lighting effects the coarse texture map exhibits incomplete areas and illumination artifacts. To resolve this we train separate UV Inpainting and UVHD diffusion models specialized for the shape-aware refinement of incomplete areas and the removal of illumination artifacts. Through this coarse-to-fine process Paint3D can produce high-quality 2K UV textures that maintain semantic consistency while being lighting-less significantly advancing the state-of-the-art in texturing 3D objects.

Recent developments in face restoration have achieved remarkable results in producing high-quality and lifelike outputs. The stunning results however often fail to be faithful with respect to the identity of the person as the models lack necessary context. In this paper we explore the potential of personalized face restoration with diffusion models. In our approach a restoration model is personalized using a few images of the identity leading to tailored restoration with respect to the identity while retaining fine-grained details. By using independent trainable blocks for personalization the rich prior of a base restoration model can be exploited to its fullest. To avoid the model relying on parts of identity left in the conditioning low-quality images a generative regularizer is employed. With a learnable parameter the model learns to balance between the details generated based on the input image and the degree of personalization. Moreover we improve the training pipeline of face restoration models to enable an alignment-free approach. We showcase the robust capabilities of our approach in several real-world scenarios with multiple identities demonstrating our method's ability to generate fine-grained details with faithful restoration. In the user study we evaluate the perceptual quality and faithfulness of the generated details with our method being voted best 61% of the time compared to the second best with 25% of the votes.

We present PEGASUS a method for constructing a personalized generative 3D face avatar from monocular video sources. Our generative 3D avatar enables disentangled controls to selectively alter the facial attributes (e.g. hair or nose) while preserving the identity. Our approach consists of two stages: synthetic database generation and constructing a personalized generative avatar. We generate a synthetic video collection of the target identity with varying facial attributes where the videos are synthesized by borrowing the attributes from monocular videos of diverse identities. Then we build a person-specific generative 3D avatar that can modify its attributes continuously while preserving its identity. Through extensive experiments we demonstrate that our method of generating a synthetic database and creating a 3D generative avatar is the most effective in preserving identity while achieving high realism. Subsequently we introduce a zero-shot approach to achieve the same goal of generative modeling more efficiently by leveraging a previously constructed personalized generative model.

We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans abundantly captured by autonomous vehicles (AV). Contrary to prior work on AV scene completion we aim to extrapolate fine geometry from unlabeled and beyond spatial limits of LiDAR scans taking a step towards generating realistic high-resolution simulation-ready 3D street environments. We propose hierarchical Generative Cellular Automata (hGCA) a spatially scalable conditional 3D generative model which grows geometry recursively with local kernels following GCAs in a coarse-to-fine manner equipped with a light-weight planner to induce global consistency. Experiments on synthetic scenes show that hGCA generates plausible scene geometry with higher fidelity and completeness compared to state-of-the-art baselines. Our model generalizes strongly from sim-to-real qualitatively outperforming baselines on the Waymo-open dataset. We also show anecdotal evidence of the ability to create novel objects from real-world geometric cues even when trained on limited synthetic content.

Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any kind of biases. However existing works focus on detecting closed sets of biases defined a priori limiting the studies to well-known concepts. In this paper we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias a new pipeline that identifies and quantifies the severity of biases agnostically without access to any precompiled set. OpenBias has three stages. In the first phase we leverage a Large Language Model (LLM) to propose biases given a set of captions. Secondly the target generative model produces images using the same set of captions. Lastly a Vision Question Answering model recognizes the presence and extent of the previously proposed biases. We study the behavior of Stable Diffusion 1.5 2 and XL emphasizing new biases never investigated before. Via quantitative experiments we demonstrate that OpenBias agrees with current closed-set bias detection methods and human judgement.

Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality significantly but have posed challenges to find the exact inverse (i.e. finding the initial noise from the given image). Here we investigate the exact inversions for DPM-solvers and propose algorithms to perform them when samples are generated by the first-order as well as higher-order DPM-solvers. For each explicit denoising step in DPM-solvers we formulated the inversions using implicit methods such as gradient descent or forward step method to ensure the robustness to large classifier-free guidance unlike the prior approach using fixed-point iteration. Experimental results demonstrated that our proposed exact inversion methods significantly reduced the error of both image and noise reconstructions greatly enhanced the ability to distinguish invisible watermarks and well prevented unintended background changes consistently during image editing.

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 .

Face morphing is a problem in computer graphics with numerous artistic and forensic applications. It is challenging due to variations in pose lighting gender and ethnicity. This task consists of a warping for feature alignment and a blending for a seamless transition between the warped images. We propose to leverage coord-based neural networks to represent such warpings and blendings of face images. During training we exploit the smoothness and flexibility of such networks by combining energy functionals employed in classical approaches without discretizations. Additionally our method is time-dependent allowing a continuous warping/blending of the images. During morphing inference we need both direct and inverse transformations of the time-dependent warping. The first (second) is responsible for warping the target (source) image into the source (target) image. Our neural warping stores those maps in a single network dismissing the need for inverting them. The results of our experiments indicate that our method is competitive with both classical and generative models under the lens of image quality and face-morphing detectors. Aesthetically the resulting images present a seamless blending of diverse faces not yet usual in the literature.

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/.

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.

We present a lightweight and affordable motion capture method based on two smartwatches and a head-mounted camera. In contrast to the existing approaches that use six or more expert-level IMU devices our approach is much more cost-effective and convenient. Our method can make wearable motion capture accessible to everyone everywhere enabling 3D full-body motion capture in diverse environments. As a key idea to overcome the extreme sparsity and ambiguities of sensor inputs with different modalities we integrate 6D head poses obtained from the head-mounted cameras for motion estimation. To enable capture in expansive indoor and outdoor scenes we propose an algorithm to track and update floor level changes to define head poses coupled with a multi-stage Transformer-based regression module. We also introduce novel strategies leveraging visual cues of egocentric images to further enhance the motion capture quality while reducing ambiguities. We demonstrate the performance of our method on various challenging scenarios including complex outdoor environments and everyday motions including object interactions and social interactions among multiple individuals.

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.

Neural Radiance Fields (NeRFs) have shown great potential in novel view synthesis. However they struggle to render sharp images when the data used for training is affected by motion blur. On the other hand event cameras excel in dynamic scenes as they measure brightness changes with microsecond resolution and are thus only marginally affected by blur. Recent methods attempt to enhance NeRF reconstructions under camera motion by fusing frames and events. However they face challenges in recovering accurate color content or constrain the NeRF to a set of predefined camera poses harming reconstruction quality in challenging conditions. This paper proposes a novel formulation addressing these issues by leveraging both model- and learning-based modules. We explicitly model the blur formation process exploiting the event double integral as an additional model-based prior. Additionally we model the event-pixel response using an end-to-end learnable response function allowing our method to adapt to non-idealities in the real event-camera sensor. We show on synthetic and real data that the proposed approach outperforms existing deblur NeRFs that use only frames as well as those that combine frames and events by +6.13dB and +2.48dB respectively.

Featurizing microscopy images for use in biological research remains a significant challenge especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond.

Layout planning spanning from architecture to interior design is a slow iterative exploration of ill-defined problems adopting a "I'll know it when I see it" approach to potential solutions. Recent advances in generative models promise automating layout generation yet often overlook the crucial role of user-guided iteration cannot generate full solutions from incomplete design ideas and do not learn for the inter-dependency of layout attributes. To address these limitations we propose MaskPLAN a novel generative model based on Graph-structured Dynamic Masked Autoencoders (GDMAE) featuring five transformers generating a blend of graph-based and image-based layout attributes. MaskPLAN lets users generate and adjust layouts with partial attribute definitions create alternatives for preferences and practice new composition-driven or functionality-driven workflows. Through cross-attribute learning and the user input as a global conditional prior we ensure that design synthesis is calibrated at every intermediate stage maintaining its feasibility and practicality. Extensive evaluations show MaskPLAN's superior performance over existing methods across multiple metrics.

Recent works have shown that generative models leave traces of their underlying generative process on the generated samples broadly referred to as fingerprints of a generative model and have studied their utility in detecting synthetic images from real ones. However the extend to which these fingerprints can distinguish between various types of synthetic image and help identify the underlying generative process remain under-explored. In particular the very definition of a fingerprint remains unclear to our knowledge. To that end in this work we formalize the definition of artifact and fingerprint in generative models propose an algorithm for computing them in practice and finally study its effectiveness in distinguishing a large array of different generative models. We find that using our proposed definition can significantly improve the performance on the task of identifying the underlying generative process from samples (model attribution) compared to existing methods. Additionally we study the structure of the fingerprints and observe that it is very predictive of the effect of different design choices on the generative process.

Recent advances in text-to-motion generation using diffusion and autoregressive models have shown promising results. However these models often suffer from a trade-off between real-time performance high fidelity and motion editability. To address this gap we introduce MMM a novel yet simple motion generation paradigm based on Masked Motion Model. MMM consists of two key components: (1) a motion tokenizer that transforms 3D human motion into a sequence of discrete tokens in latent space and (2) a conditional masked motion transformer that learns to predict randomly masked motion tokens conditioned on the pre-computed text tokens. By attending to motion and text tokens in all directions MMM explicitly captures inherent dependency among motion tokens and semantic mapping between motion and text tokens. During inference this allows parallel and iterative decoding of multiple motion tokens that are highly consistent with fine-grained text descriptions therefore simultaneously achieving high-fidelity and high-speed motion generation. In addition MMM has innate motion editability. By simply placing mask tokens in the place that needs editing MMM automatically fills the gaps while guaranteeing smooth transitions between editing and non-editing parts. Extensive experiments on the HumanML3D and KIT-ML datasets demonstrate that MMM surpasses current leading methods in generating high-quality motion (evidenced by superior FID scores of 0.08 and 0.429) while offering advanced editing features such as body-part modification motion in-betweening and the synthesis of long motion sequences. In addition MMM is two orders of magnitude faster on a single mid-range GPU than editable motion diffusion models. Our project page is available at https://exitudio.github.io/MMM-page/.