Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet the first dataset tailored to the preferences of young audiences enabling the generation of aesthetically oriented dances. And it surpasses the AIST++ dataset in music genre diversity and the intricacy and depth of dance movements. Moreover the proposed POPDG model within the iDDPM framework enhances dance diversity and through the Space Augmentation Algorithm strengthens spatial physical connections between human body joints ensuring that increased diversity does not compromise generation quality. A streamlined Alignment Module is also designed to improve the temporal alignment between dance and music. Extensive experiments show that POPDG achieves SOTA results on two datasets. Furthermore the paper also expands on current evaluation metrics. The dataset and code are available at https://github.com/Luke-Luo1/POPDG.
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Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focus on geometry reasoning and neglect the actual physical assembly process of matching and fitting joints which are the contact surfaces connecting different parts. In this paper we consider contacting joints for the task of multi-part assembly. A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment. We propose a hierarchical graph learning approach composed of two levels of graph representation learning. The part graph takes part geometries as input to build the desired shape structure. The joint-level graph uses part joints information and focuses on matching and aligning joints. The two kinds of information are combined to achieve the bilateral objectives. Extensive experiments demonstrate that our method outperforms previous methods achieving better shape structure and higher joint alignment accuracy.
UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures
PDF ↗Recent advances in 3D avatar generation have gained significant attention. These breakthroughs aim to produce more realistic animatable avatars narrowing the gap between virtual and real-world experiences. Most of existing works employ Score Distillation Sampling (SDS) loss combined with a differentiable renderer and text condition to guide a diffusion model in generating 3D avatars. However SDS often generates over-smoothed results with few facial details thereby lacking the diversity compared with ancestral sampling. On the other hand other works generate 3D avatar from a single image where the challenges of unwanted lighting effects perspective views and inferior image quality make them difficult to reliably reconstruct the 3D face meshes with the aligned complete textures. In this paper we propose a novel 3D avatar generation approach termed UltrAvatar with enhanced fidelity of geometry and superior quality of physically based rendering (PBR) textures without unwanted lighting. To this end the proposed approach presents a diffuse color extraction model and an authenticity guided texture diffusion model. The former removes the unwanted lighting effects to reveal true diffuse colors so that the generated avatars can be rendered under various lighting conditions. The latter follows two gradient-based guidances for generating PBR textures to render diverse face-identity features and details better aligning with 3D mesh geometry. We demonstrate the effectiveness and robustness of the proposed method outperforming the state-of-the-art methods by a large margin in the experiments.
We present DiffuScene for indoor 3D scene synthesis based on a novel scene configuration denoising diffusion model. It generates 3D instance properties stored in an unordered object set and retrieves the most similar geometry for each object configuration which is characterized as a concatenation of different attributes including location size orientation semantics and geometry features. We introduce a diffusion network to synthesize a collection of 3D indoor objects by denoising a set of unordered object attributes. Unordered parametrization simplifies and eases the joint distribution approximation. The shape feature diffusion facilitates natural object placements including symmetries. Our method enables many downstream applications including scene completion scene arrangement and text-conditioned scene synthesis. Experiments on the 3D-FRONT dataset show that our method can synthesize more physically plausible and diverse indoor scenes than state-of-the-art methods. Extensive ablation studies verify the effectiveness of our design choice in scene diffusion models.
The recently increased role of mobile photography has raised the standards of on-device photo processing tremendously. Despite the latest advancements in camera hardware the mobile camera sensor area cannot be increased significantly due to physical constraints leading to a pixel size of 0.6--2.0 \mum which results in strong image noise even in moderate lighting conditions. In the era of deep learning one can train a CNN model to perform robust image denoising. However there is still a lack of a substantially diverse dataset for this task. To address this problem we introduce a novel Mobile Image Denoising Dataset (MIDD) comprising over 400000 noisy / noise-free image pairs captured under various conditions by 20 different mobile camera sensors. Additionally we propose a new DPreview test set consisting of data from 294 different cameras for precise model evaluation. Furthermore we present the efficient baseline model SplitterNet for the considered mobile image denoising task that achieves high numerical and visual results while being able to process 8MP photos directly on smartphone GPUs in under one second. Thereby outperforming models with similar runtimes. This model is also compatible with recent mobile NPUs demonstrating an even higher speed when deployed on them. The conducted experiments demonstrate high robustness of the proposed solution when applied to images from previously unseen sensors showing its high generalizability. The datasets code and models can be found on the official project website.
We present HAAR a new strand-based generative model for 3D human hairstyles. Specifically based on textual inputs HAAR produces 3D hairstyles that could be used as production-level assets in modern computer graphics engines. Current AI-based generative models take advantage of powerful 2D priors to reconstruct 3D content in the form of point clouds meshes or volumetric functions. However by using the 2D priors they are intrinsically limited to only recovering the visual parts. Highly occluded hair structures can not be reconstructed with those methods and they only model the "outer shell" which is not ready to be used in physics-based rendering or simulation pipelines. In contrast we propose a first text-guided generative method that uses 3D hair strands as an underlying representation. Leveraging 2D visual question-answering (VQA) systems we automatically annotate synthetic hair models that are generated from a small set of artist-created hairstyles. This allows us to train a latent diffusion model that operates in a common hairstyle UV space. In qualitative and quantitative studies we demonstrate the capabilities of the proposed model and compare it to existing hairstyle generation approaches. For results please refer to our project page https://haar.is.tue.mpg.de/.
Generating human motions from textual descriptions has gained growing research interest due to its wide range of applications. However only a few works consider human-scene interactions together with text conditions which is crucial for visual and physical realism. This paper focuses on the task of generating human motions in 3D indoor scenes given text descriptions of the human-scene interactions. This task presents challenges due to the multimodality nature of text scene and motion as well as the need for spatial reasoning. To address these challenges we propose a new approach that decomposes the complex problem into two more manageable sub-problems: (1) language grounding of the target object and (2) object-centric motion generation. For language grounding of the target object we leverage the power of large language models. For motion generation we design an object-centric scene representation for the generative model to focus on the target object thereby reducing the scene complexity and facilitating the modeling of the relationship between human motions and the object. Experiments demonstrate the better motion quality of our approach compared to baselines and validate our design choices. Code will be available at https://zju3dv.github.io/text_scene_motion.
Vision-language navigation (VLN) requires an agent to navigate through an 3D environment based on visual observations and natural language instructions. It is clear that the pivotal factor for successful navigation lies in the comprehensive scene understanding. Previous VLN agents employ monocular frameworks to extract 2D features of perspective views directly. Though straightforward they struggle for capturing 3D geometry and semantics leading to a partial and incomplete environment representation. To achieve a comprehensive 3D representation with fine-grained details we introduce a Volumetric Environment Representation (VER) which voxelizes the physical world into structured 3D cells. For each cell VER aggregates multi-view 2D features into such a unified 3D space via 2D-3D sampling. Through coarse-to-fine feature extraction and multi-task learning for VER our agent predicts 3D occupancy 3D room layout and 3D bounding boxes jointly. Based on online collected VERs our agent performs volume state estimation and builds episodic memory for predicting the next step. Experimental results show our environment representations from multi-task learning lead to evident performance gains on VLN. Our model achieves state-of-the-art performance across VLN benchmarks (R2R REVERIE and R4R).
SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples
PDF ↗While vision-language models (VLMs) have achieved remarkable performance improvements recently there is growing evidence that these models also posses harmful biases with respect to social attributes such as gender and race. Prior studies have primarily focused on probing such bias attributes individually while ignoring biases associated with intersections between social attributes. This could be due to the difficulty of collecting an exhaustive set of image-text pairs for various combinations of social attributes. To address this challenge we employ text-to-image diffusion models to produce counterfactual examples for probing intersectional social biases at scale. Our approach utilizes Stable Diffusion with cross attention control to produce sets of counterfactual image-text pairs that are highly similar in their depiction of a subject (e.g. a given occupation) while differing only in their depiction of intersectional social attributes (e.g. race & gender). Through our over-generate-then-filter methodology we produce SocialCounterfactuals a high-quality dataset containing 171k image-text pairs for probing intersectional biases related to gender race and physical characteristics. We conduct extensive experiments to demonstrate the usefulness of our generated dataset for probing and mitigating intersectional social biases in state-of-the-art VLMs.
Phase unwrapping (PU) is a technique to reconstruct original phase images from their noisy wrapped counterparts finding many applications in scientific imaging. Although supervised learning has shown promise in PU its utility is limited in ground-truth (GT) scarce scenarios. This paper presents an unsupervised learning approach that eliminates the need for GTs during end-to-end training. Our approach leverages the insight that both the gradients and wrapped gradients of wrapped phases serve as noisy labels for GT phase gradients along with sparse outliers induced by the wrapping operation. A recorruption-based self-reconstruction loss in the gradient domain is proposed to mitigate the adverse effects of label noise complemented with a self-distillation loss for improved generalization. Additionally by unfolding a variational model of PU that utilizes wrapped gradients of wrapped phases for its data-fitting term we develop a deep unrolling network that encodes physics of phase wrapping and incorporates special treatments on outliers. In the experiments on three types of phase data our approach outperforms existing GT-free methods and competes well against the supervised ones.
Multi-agent trajectory prediction is essential in autonomous driving risk avoidance and traffic flow control. However the heterogeneous traffic density on interactions which caused by physical laws social norms and so on is often overlooked in existing methods. When the density varies the number of agents involved in interactions and the corresponding interaction probability change dynamically. To tackle this issue we propose a new method called \underline D ensity-\underline A daptive Model based on \underline M otif \underline M atrix for Multi-Agent Trajectory Prediction (DAMM) to gain insights into multi-agent systems. Here we leverage the motif matrix to represent dynamic connectivity in a higher-order pattern and distill the interaction information from the perspectives of the spatial and the temporal dimensions. Specifically in spatial dimension we utilize multi-scale feature fusion to adaptively select the optimal range of neighbors participating in interactions for each time slot. In temporal dimension we extract the temporal interaction features and adapt a pyramidal pooling layer to generate the interaction probability for each agent. Experimental results demonstrate that our approach surpasses state-of-the-art methods on autonomous driving dataset.
Long-term and accurate forecasting is the long-standing pursuit of the human motion prediction task. Existing methods typically suffer from dramatic degradation in prediction accuracy with the increasing prediction horizon. It comes down to two reasons:1? Insufficient numerical stability.Unforeseen high noise and complex feature relationships in the data. 2? Inadequate modeling stability. Unreasonable step sizes and undesirable parameter updates in the prediction.In this paper we design a novel and symplectic integral-inspired framework named symplectic integral neural network (SINN) which engages symplectic trajectories to optimize the pose representation and employs a stable symplectic operator to alternately model the dynamic context. Specifically we design a Symplectic Representation Encoder that performs on enhanced human pose representation to obtain trajectories on the symplectic manifold ensuring numerical stability based on Hamiltonian mechanics and symplectic spatial splitting algorithm. We further present the Symplectic Temporal Aggregation module in the light of the symplectic temporal splitting algorithm which splits the long-term prediction into multiple accurate short-term predictions generated by a symplectic operator to secure modeling stability. Moreover our approach is model-agnostic and can be efficiently integrated with different physical dynamics models.The experimental results demonstrate that our method achieves the new state-of-the-art outperforming existing methods by large margins:20.1%on Human3.6M16.7%on CUM Mocap and 10.2% on 3DPW.
Infrared physical adversarial examples are of great significance for studying the security of infrared AI systems that are widely used in our lives such as autonomous driving. Previous infrared physical attacks mainly focused on 2D infrared pedestrian detection which may not fully manifest its destructiveness to AI systems. In this work we propose a physical attack method against infrared detectors based on 3D modeling which is applied to a real car. The goal is to design a set of infrared adversarial stickers to make cars invisible to infrared detectors at various viewing angles distances and scenes. We build a 3D infrared car model with real infrared characteristics and propose an infrared adversarial pattern generation method based on 3D mesh shadow. We propose a 3D control points-based mesh smoothing algorithm and use a set of smoothness loss functions to enhance the smoothness of adversarial meshes and facilitate the sticker implementation. Besides We designed the aluminum stickers and conducted physical experiments on two real Mercedes-Benz A200L cars. Our adversarial stickers hid the cars from Faster RCNN an object detector at various viewing angles distances and scenes. The attack success rate (ASR) was 91.49% for real cars. In comparison the ASRs of random stickers and no sticker were only 6.21% and 0.66% respectively. In addition the ASRs of the designed stickers against six unseen object detectors such as YOLOv3 and Deformable DETR were between 73.35%-95.80% showing good transferability of the attack performance across detectors.
Aligned text-image encoders such as CLIP have become the de-facto model for vision-language tasks. Furthermore modality-specific encoders achieve impressive performances in their respective domains. This raises a central question: does an alignment exist between uni-modal vision and language encoders since they fundamentally represent the same physical world? Analyzing the latent spaces structure of vision and language models on image-caption benchmarks using the Centered Kernel Alignment (CKA) we find that the representation spaces of unaligned and aligned encoders are semantically similar. In the absence of statistical similarity in aligned encoders like CLIP we show that a possible matching of unaligned encoders exists without any training. We frame this as a seeded graph-matching problem exploiting the semantic similarity between graphs and propose two methods - a Fast Quadratic Assignment Problem optimization and a novel localized CKA metric-based matching/retrieval. We demonstrate the effectiveness of this on several downstream tasks including cross-lingual cross-domain caption matching and image classification. Code available at github.com/mayug/0-shot-llm-vision.
Can computers perceive the physical properties of objects solely through vision? Research in cognitive science and vision science has shown that humans excel at identifying materials and estimating their physical properties based purely on visual appearance. In this paper we present a novel approach for dense prediction of the physical properties of objects using a collection of images. Inspired by how humans reason about physics through vision we leverage large language models to propose candidate materials for each object. We then construct a language-embedded point cloud and estimate the physical properties of each 3D point using a zero-shot kernel regression approach. Our method is accurate annotation-free and applicable to any object in the open world. Experiments demonstrate the effectiveness of the proposed approach in various physical property reasoning tasks such as estimating the mass of common objects as well as other properties like friction and hardness.
We introduce pix2gestalt a framework for zero-shot amodal segmentation which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions. By capitalizing on large-scale diffusion models and transferring their representations to this task we learn a conditional diffusion model for reconstructing whole objects in challenging zero-shot cases including examples that break natural and physical priors such as art. As training data we use a synthetically curated dataset containing occluded objects paired with their whole counterparts. Experiments show that our approach outperforms supervised baselines on established benchmarks. Our model can furthermore be used to significantly improve the performance of existing object recognition and 3D reconstruction methods in the presence of occlusions.
We investigate a new task in human motion prediction which is predicting motions under unexpected physical perturbation potentially involving multiple people. Compared with existing research this task involves predicting less controlled unpremeditated and pure reactive motions in response to external impact and how such motions can propagate through people. It brings new challenges such as data scarcity and predicting complex interactions. To this end we propose a new method capitalizing differentiable physics and deep neural networks leading to an explicit Latent Differentiable Physics (LDP) model. Through experiments we demonstrate that LDP has high data efficiency outstanding prediction accuracy strong generalizability and good explainability. Since there is no similar research a comprehensive comparison with 11 adapted baselines from several relevant domains is conducted showing LDP outperforming existing research both quantitatively and qualitatively improving prediction accuracy by as much as 70% and demonstrating significantly stronger generalization.
SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting
PDF ↗We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore to overcome the limitation of scarce high-quality lightstage data we have developed a self-supervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism.
Compared with transferable untargeted attacks transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples posing a greater threat to security-critical tasks. In the meanwhile 3D adversarial samples due to their potential of multi-view robustness can more comprehensively identify weaknesses in existing deep learning systems possessing great application value. However the field of transferable targeted 3D adversarial attacks remains vacant. The goal of this work is to develop a more effective technique that could generate transferable targeted 3D adversarial examples filling the gap in this field. To achieve this goal we design a novel framework named TT3D that could rapidly reconstruct from few multi-view images into Transferable Targeted 3D textured meshes. While existing mesh-based texture optimization methods compute gradients in the high-dimensional mesh space and easily fall into local optima leading to unsatisfactory transferability and distinct distortions TT3D innovatively performs dual optimization towards both feature grid and Multi-layer Perceptron (MLP) parameters in the grid-based NeRF space which significantly enhances black-box transferability while enjoying naturalness. Experimental results show that TT3D not only exhibits superior cross-model transferability but also maintains considerable adaptability across different renders and vision tasks. More importantly we produce 3D adversarial examples with 3D printing techniques in the real world and verify their robust performance under various scenarios.
Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives
PDF ↗We propose Lodge a network capable of generating extremely long dance sequences conditioned on given music. We design Lodge as a two-stage coarse to fine diffusion architecture and propose the characteristic dance primitives that possess significant expressiveness as intermediate representations between two diffusion models. The first stage is global diffusion which focuses on comprehending the coarse-level music-dance correlation and production characteristic dance primitives. In contrast the second-stage is the local diffusion which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules. In addition we propose a Foot Refine Block to optimize the contact between the feet and the ground enhancing the physical realism of the motion. Code available at https://li-ronghui.github.io/lodge