CVPR 2024 Papers

Inverse rendering aims at recovering both geometry and materials of objects. It provides a more compatible reconstruction for conventional rendering engines compared with the neural radiance fields (NeRFs). On the other hand existing NeRF-based inverse rendering methods cannot handle glossy objects with local light interactions well as they typically oversimplify the illumination as a 2D environmental map which assumes infinite lights only. Observing the superiority of NeRFs in recovering radiance fields we propose a novel 5D Neural Plenoptic Function (NeP) based on NeRFs and ray tracing such that more accurate lighting-object interactions can be formulated via the rendering equation. We also design a material-aware cone sampling strategy to efficiently integrate lights inside the BRDF lobes with the help of pre-filtered radiance fields. Our method has two stages: the geometry of the target object and the pre-filtered environmental radiance fields are reconstructed in the first stage and materials of the target object are estimated in the second stage with the proposed NeP and material-aware cone sampling strategy. Extensive experiments on the proposed real-world and synthetic datasets demonstrate that our method can reconstruct high-fidelity geometry/materials of challenging glossy objects with complex lighting interactions from nearby objects. Project webpage: https://whyy.site/paper/nep

We present IntrinsicAvatar a novel approach to recovering the intrinsic properties of clothed human avatars including geometry albedo material and environment lighting from only monocular videos. Recent advancements in human-based neural rendering have enabled high-quality geometry and appearance reconstruction of clothed humans from just monocular videos. However these methods bake intrinsic properties such as albedo material and environment lighting into a single entangled neural representation. On the other hand only a handful of works tackle the problem of estimating geometry and disentangled appearance properties of clothed humans from monocular videos. They usually achieve limited quality and disentanglement due to approximations of secondary shading effects via learned MLPs. In this work we propose to model secondary shading effects explicitly via Monte-Carlo ray tracing. We model the rendering process of clothed humans as a volumetric scattering process and combine ray tracing with body articulation. Our approach can recover high-quality geometry albedo material and lighting properties of clothed humans from a single monocular video without requiring supervised pre-training using ground truth materials. Furthermore since we explicitly model the volumetric scattering process and ray tracing our model naturally generalizes to novel poses enabling animation of the reconstructed avatar in novel lighting conditions.

We present Intrinsic Image Diffusion a generative model for appearance decomposition of indoor scenes. Given a single input view we sample multiple possible material explanations represented as albedo roughness and metallic maps. Appearance decomposition poses a considerable challenge in computer vision due to the inherent ambiguity between lighting and material properties and the lack of real datasets. To address this issue we advocate for a probabilistic formulation where instead of attempting to directly predict the true material properties we employ a conditional generative model to sample from the solution space. Furthermore we show that utilizing the strong learned prior of recent diffusion models trained on large-scale real-world images can be adapted to material estimation and highly improves the generalization to real images. Our method produces significantly sharper more consistent and more detailed materials outperforming state-of-the-art methods by 1.5dB on PSNR and by 45% better FID score on albedo prediction. We demonstrate the effectiveness of our approach through experiments on both synthetic and real-world datasets.

Denoising probabilistic diffusion models have shown breakthrough performance to generate more photo-realistic images or human-level illustrations than the prior models such as GANs. This high image-generation capability has stimulated the creation of many downstream applications in various areas. However we find that this technology is actually a double-edged sword: We identify a new type of attack called the Natural Denoising Diffusion (NDD) attack based on the finding that state-of-the-art deep neural network (DNN) models still hold their prediction even if we intentionally remove their robust features which are essential to the human visual system (HVS) through text prompts. The NDD attack shows a significantly high capability to generate low-cost model-agnostic and transferable adversarial attacks by exploiting the natural attack capability in diffusion models. To systematically evaluate the risk of the NDD attack we perform a large-scale empirical study with our newly created dataset the Natural Denoising Diffusion Attack (NDDA) dataset. We evaluate the natural attack capability by answering 6 research questions. Through a user study we find that it can achieve an 88% detection rate while being stealthy to 93% of human subjects; we also find that the non-robust features embedded by diffusion models contribute to the natural attack capability. To confirm the model-agnostic and transferable attack capability we perform the NDD attack against the Tesla Model 3 and find that 73% of the physically printed attacks can be detected as stop signs. Our hope is that the study and dataset can help our community be aware of the risks in diffusion models and facilitate further research toward robust DNN models.

Surgical decisions are informed by aligning rapid portable 2D intraoperative images (e.g. X-rays) to a high-fidelity 3D preoperative reference scan (e.g. CT). However 2D/3D registration can often fail in practice: conventional optimization methods are prohibitively slow and susceptible to local minima while neural networks trained on small datasets fail on new patients or require impractical landmark supervision. We present DiffPose a self-supervised approach that leverages patient-specific simulation and differentiable physics-based rendering to achieve accurate 2D/3D registration without relying on manually labeled data. Preoperatively a CNN is trained to regress the pose of a randomly oriented synthetic X-ray rendered from the preoperative CT. The CNN then initializes rapid intraoperative test-time optimization that uses the differentiable X-ray renderer to refine the solution. Our work further proposes several geometrically principled methods for sampling camera poses from SE(3) for sparse differentiable rendering and for driving registration in the tangent space se(3) with geodesic and multiscale locality-sensitive losses. DiffPose achieves sub-millimeter accuracy across surgical datasets at intraoperative speeds improving upon existing unsupervised methods by an order of magnitude and even outperforming supervised baselines. Our implementation is at https://github.com/eigenvivek/DiffPose.

Quantifying the degree of similarity between images is a key copyright issue for image-based machine learning. In legal doctrine however determining the degree of similarity between works requires subjective analysis and fact-finders (judges and juries) can demonstrate considerable variability in these subjective judgement calls. Images that are structurally similar can be deemed dissimilar whereas images of completely different scenes can be deemed similar enough to support a claim of copying. We seek to define and compute a notion of "conceptual similarity" among images that captures high-level relations even among images that do not share repeated elements or visually similar components. The idea is to use a base multi-modal model to generate "explanations" (captions) of visual data at increasing levels of complexity. Then similarity can be measured by the length of the caption needed to discriminate between the two images: Two highly dissimilar images can be discriminated early in their description whereas conceptually dissimilar ones will need more detail to be distinguished. We operationalize this definition and show that it correlates with subjective (averaged human evaluation) assessment and beats existing baselines on both image-to-image and text-to-text similarity benchmarks. Beyond just providing a number our method also offers interpretability by pointing to the specific level of granularity of the description where the source data is differentiated.

The exponential growth of large language models (LLMs) has opened up numerous possibilities for multi-modal AGI systems. However the progress in vision and vision-language foundation models which are also critical elements of multi-modal AGI has not kept pace with LLMs. In this work we design a large-scale vision-language foundation model (InternVL) which scales up the vision foundation model to 6 billion parameters and progressively aligns it with the LLM using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks including visual perception tasks such as image-level or pixel-level recognition vision-language tasks such as zero-shot image/video classification zero-shot image/video-text retrieval and link with LLMs to create multi-modal dialogue systems. It has powerful visual capabilities and can be a good alternative to the ViT-22B. We hope that our research could contribute to the development of multi-modal large models.

3D object generation has undergone significant advancements yielding high-quality results. However fall short in achieving precise user control often yielding results that do not align with user expectations thus limiting their applicability. User-envisioning 3D object generation faces significant challenges in realizing its concepts using current generative models due to limited interaction capabilities. Existing methods mainly offer two approaches: (i) interpreting textual instructions with constrained controllability or (ii) reconstructing 3D objects from 2D images. Both of them limit customization to the confines of the 2D reference and potentially introduce undesirable artifacts during the 3D lifting process restricting the scope for direct and versatile 3D modifications. In this work we introduce Interactive3D an innovative framework for interactive 3D generation that grants users precise control over the generative process through extensive 3D interaction capabilities. Interactive3D is constructed in two cascading stages utilizing distinct 3D representations. The first stage employs Gaussian Splatting for direct user interaction allowing modifications and guidance of the generative direction at any intermediate step through (i) Adding and Removing components (ii) Deformable and Rigid Dragging (iii) Geometric Transformations and (iv) Semantic Editing. Subsequently the Gaussian splats are transformed into InstantNGP. We introduce a novel (v) Interactive Hash Refinement module to further add details and extract the geometry in the second stage. Our experiments demonstrate that proposed Interactive3D markedly improves the controllability and quality of 3D generation. Our project webpage is available at https://interactive-3d.github.io/.

Advanced life forms sustained by the synergistic interaction of neural cognitive mechanisms continually acquire and transfer knowledge throughout their lifespan. In contrast contemporary machine learning paradigms exhibit limitations in emulating the facets of continual learning (CL). Nonetheless the emergence of large language models (LLMs) presents promising avenues for realizing CL via interactions with these models. Drawing on Complementary Learning System theory this paper presents a novel Interactive Continual Learning (ICL) framework enabled by collaborative interactions among models of various sizes. Specifically we assign the ViT model as System1 and multimodal LLM as System2. To enable the memory module to deduce tasks from class information and enhance Set2Set retrieval we propose the Class-Knowledge-Task Multi-Head Attention (CKT-MHA). Additionally to improve memory retrieval in System1 through enhanced geometric representation we introduce the CL-vMF mechanism based on the von Mises-Fisher (vMF) distribution. Meanwhile we introduce the von Mises-Fisher Outlier Detection and Interaction (vMF-ODI) strategy to identify hard examples thus enhancing collaboration between System1 and System2 for complex reasoning realization. Comprehensive evaluation of our proposed ICL demonstrates significant resistance to forgetting and superior performance relative to existing methods. Code is available at github.com/ICL.

Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions enabling vast applications in content generation. While recent advancements have introduced control over factors such as object localization posture and image contours a crucial gap remains in our ability to control the interactions between objects in the generated content. Well-controlling interactions in generated images could yield meaningful applications such as creating realistic scenes with interacting characters. In this work we study the problems of conditioning T2I diffusion models with Human-Object Interaction (HOI) information consisting of a triplet label (person action object) and corresponding bounding boxes. We propose a pluggable interaction control model called InteractDiffusion that extends existing pre-trained T2I diffusion models to enable them being better conditioned on interactions. Specifically we tokenize the HOI information and learn their relationships via interaction embeddings. A conditioning self-attention layer is trained to map HOI tokens to visual tokens thereby conditioning the visual tokens better in existing T2I diffusion models. Our model attains the ability to control the interaction and location on existing T2I diffusion models which outperforms existing baselines by a large margin in HOI detection score as well as fidelity in FID and KID. Project page: https://jiuntian.github.io/interactdiffusion.

We present InterHandGen a novel framework that learns the generative prior of two-hand interaction. Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object. Our prior can be incorporated into any optimization or learning methods to reduce ambiguity in an ill-posed setup. Our key observation is that directly modeling the joint distribution of multiple instances imposes high learning complexity due to its combinatorial nature. Thus we propose to decompose the modeling of joint distribution into the modeling of factored unconditional and conditional single instance distribution. In particular we introduce a diffusion model that learns the single-hand distribution unconditional and conditional to another hand via conditioning dropout. For sampling we combine anti-penetration and classifier-free guidance to enable plausible generation. Furthermore we establish the rigorous evaluation protocol of two-hand synthesis where our method significantly outperforms baseline generative models in terms of plausibility and diversity. We also demonstrate that our diffusion prior can boost the performance of two-hand reconstruction from monocular in-the-wild images achieving new state-of-the-art accuracy.

The analysis of the ubiquitous human-human interactions is pivotal for understanding humans as social beings. Existing human-human interaction datasets typically suffer from inaccurate body motions lack of hand gestures and fine-grained textual descriptions. To better perceive and generate human-human interactions we propose Inter-X a currently largest human-human interaction dataset with accurate body movements and diverse interaction patterns together with detailed hand gestures. The dataset includes 11K interaction sequences and more than 8.1M frames. We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions semantic interaction categories interaction order and the relationship and personality of the subjects. Based on the elaborate annotations we propose a unified benchmark composed of 4 categories of downstream tasks from both the perceptual and generative directions. Extensive experiments and comprehensive analysis show that Inter-X serves as a testbed for promoting the development of versatile human-human interaction analysis. Our dataset and benchmark will be publicly available for research purposes.

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

Generative models have recently exhibited exceptional capabilities in text-to-image generation but still struggle to generate image sequences coherently. In this work we focus on a novel yet challenging task of generating a coherent image sequence based on a given storyline denoted as open-ended visual storytelling. We make the following three contributions: (i) to fulfill the task of visual storytelling we propose a learning-based auto-regressive image generation model termed as StoryGen with a novel vision-language context module that enables to generate the current frame by conditioning on the corresponding text prompt and preceding image-caption pairs; (ii) to address the data shortage of visual storytelling we collect paired image-text sequences by sourcing from online videos and open-source E-books establishing processing pipeline for constructing a large-scale dataset with diverse characters storylines and artistic styles named StorySalon; (iii) Quantitative experiments and human evaluations have validated the superiority of our StoryGen where we show it can generalize to unseen characters without any optimization and generate image sequences with coherent content and consistent character. Code dataset and models are available at https://haoningwu3639.github.io/StoryGen_Webpage/

In the realm of computer vision and graphics accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking registration texture transfer and statistical shape analysis. Moving beyond traditional hand-crafted and data-driven feature learning methods we incorporate spectral methods with deep learning focusing on functional maps (FMs) and optimal transport (OT). Traditional OT-based approaches often reliant on entropy regularization OT in learning-based framework face computational challenges due to their quadratic cost. Our key contribution is to employ the sliced Wasserstein distance (SWD) for OT which is a valid fast optimal transport metric in an unsupervised shape matching framework. This unsupervised framework integrates functional map regularizers with a novel OT-based loss derived from SWD enhancing feature alignment between shapes treated as discrete probability measures. We also introduce an adaptive refinement process utilizing entropy regularized OT further refining feature alignments for accurate point-to-point correspondences. Our method demonstrates superior performance in non-rigid shape matching including near-isometric and non-isometric scenarios and excels in downstream tasks like segmentation transfer. The empirical results on diverse datasets highlight our framework's effectiveness and generalization capabilities setting new standards in non-rigid shape matching with efficient OT metrics and an adaptive refinement module.

Diffusion models have emerged as the de facto paradigm for video generation. However their reliance on web-scale data of varied quality often yields results that are visually unappealing and misaligned with the textual prompts. To tackle this problem we propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning. InstructVideo has two key ingredients: 1) To ameliorate the cost of reward fine-tuning induced by generating through the full DDIM sampling chain we recast reward fine-tuning as editing. By leveraging the diffusion process to corrupt a sampled video InstructVideo requires only partial inference of the DDIM sampling chain reducing fine-tuning cost while improving fine-tuning efficiency. 2) To mitigate the absence of a dedicated video reward model for human preferences we repurpose established image reward models e.g. HPSv2. To this end we propose Segmental Video Reward a mechanism to provide reward signals based on segmental sparse sampling and Temporally Attenuated Reward a method that mitigates temporal modeling degradation during fine-tuning. Extensive experiments both qualitative and quantitative validate the practicality and efficacy of using image reward models in InstructVideo significantly enhancing the visual quality of generated videos without compromising generalization capabilities. Code and models can be accessed through our project page https://instructvideo.github.io/.

We present InstructDiffusion a unified and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge and pre-define the output space (e.g. categories and coordinates) for each vision task we cast diverse vision tasks into a human-intuitive image-manipulating process whose output space is a flexible and interactive pixel space. Concretely the model is built upon the diffusion process and is trained to predict pixels according to user instructions such as encircling the man's left shoulder in red or applying a blue mask to the left car. InstructDiffusion could handle a variety of vision tasks including understanding tasks (such as segmentation and keypoint detection) and generative tasks (such as editing and enhancement) and outperforms prior methods on novel datasets. This represents a solid step towards a generalist modeling interface for vision tasks advancing artificial general intelligence in the field of computer vision.

Human intelligence can retrieve any person according to both visual and language descriptions. However the current computer vision community studies specific person re-identification (ReID) tasks in different scenarios separately which limits the applications in the real world. This paper strives to resolve this problem by proposing a new instruct-ReID task that requires the model to retrieve images according to the given image or language instructions. Our instruct-ReID is a more general ReID setting where existing 6 ReID tasks can be viewed as special cases by designing different instructions. We propose a large-scale OmniReID benchmark and an adaptive triplet loss as a baseline method to facilitate research in this new setting. Experimental results show that the proposed multi-purpose ReID model trained on our OmniReID benchmark without finetuning can improve +0.5% +0.6% +7.7% mAP on Market1501 MSMT17 CUHK03 for traditional ReID +6.4% +7.1% +11.2% mAP on PRCC VC-Clothes LTCC for clothes-changing ReID +11.7% mAP on COCAS+ real2 for clothes template based clothes-changing ReID when using only RGB images +24.9% mAP on COCAS+ real2 for our newly defined language-instructed ReID +4.3% on LLCM for visible-infrared ReID +2.6% on CUHK-PEDES for text-to-image ReID. The datasets the model and code are available at https://github.com/hwz-zju/Instruct-ReID.

This paper presents Instruct-Imagen a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce multi-modal instruction for image generation a task representation articulating a range of generation intents with precision. It uses natural language to amalgamate disparate modalities (e.g. text edge style subject etc.) such that abundant generation intents can be standardized in a uniform format. We then build Instruct-Imagen by fine-tuning a pre-trained text-to-image diffusion model with two stages. First we adapt the model using the retrieval-augmented training to enhance model's capabilities to ground its generation on external multi-modal context. Subsequently we fine-tune the adapted model on diverse image generation tasks that requires vision-language understanding (e.g. subject-driven generation etc.) each paired with a multi-modal instruction encapsulating the task's essence. Human evaluation on various image generation datasets reveals that Instruct-Imagen matches or surpasses prior task-specific models in-domain and demonstrates promising generalization to unseen and more complex tasks. Our evaluation suite will be made publicly available.