Traditional approaches in physics-based motion generation centered around imitation learning and reward shaping often struggle to adapt to new scenarios. To tackle this limitation we propose AnySkill a novel hierarchical method that learns physically plausible interactions following open-vocabulary instructions. Our approach begins by developing a set of atomic actions via a low-level controller trained via imitation learning. Upon receiving an open-vocabulary textual instruction AnySkill employs a high-level policy that selects and integrates these atomic actions to maximize the CLIP similarity between the agent's rendered images and the text. An important feature of our method is the use of image-based rewards for the high-level policy which allows the agent to learn interactions with objects without manual reward engineering. We demonstrate AnySkill's capability to generate realistic and natural motion sequences in response to unseen instructions of varying lengths marking it the first method capable of open-vocabulary physical skill learning for interactive humanoid agents.
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We present GauHuman a 3D human model with Gaussian Splatting for both fast training (1 2 minutes) and real-time rendering (up to 189 FPS) compared with existing NeRF-based implicit representation modelling frameworks demanding hours of training and seconds of rendering per frame. Specifically GauHuman encodes Gaussian Splatting in the canonical space and transforms 3D Gaussians from canonical space to posed space with linear blend skinning (LBS) in which effective pose and LBS refinement modules are designed to learn fine details of 3D humans under negligible computational cost. Moreover to enable fast optimization of GauHuman we initialize and prune 3D Gaussians with 3D human prior while splitting/cloning via KL divergence guidance along with a novel merge operation for further speeding up. Extensive experiments on ZJU_Mocap and MonoCap datasets demonstrate that GauHuman achieves state-of-the-art performance quantitatively and qualitatively with fast training and real-time rendering speed. Notably without sacrificing rendering quality GauHuman can fast model the 3D human performer with 13k 3D Gaussians. Our code is available at https://github.com/skhu101/GauHuman.
The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes. However even though the image and language representations have been aligned by cross-modal models like CLIP we find that the image modality fails to contribute as much as the language in existing multi-modal 3D representation learning methods. This is attributed to the domain shift in the 2D images and the distinct focus of each modality. To more effectively leverage both modalities in the pre-training we introduce TriAdapter Multi-Modal Learning (TAMM) - a novel two-stage learning approach based on three synergistic adapters. First our CLIP Image Adapter mitigates the domain gap between 3D-rendered images and natural images by adapting the visual representations of CLIP for synthetic image-text pairs. Subsequently our Dual Adapters decouple the 3D shape representation space into two complementary sub-spaces: one focusing on visual attributes and the other for semantic understanding which ensure a more comprehensive and effective multi-modal pre-training. Extensive experiments demonstrate that TAMM consistently enhances 3D representations for a wide range of 3D encoder architectures pre-training datasets and downstream tasks. Notably we boost the zero-shot classification accuracy on Objaverse-LVIS from 46.8% to 50.7% and improve the 5-way 10-shot linear probing classification accuracy on ModelNet40 from 96.1% to 99.0%. Project page: https://alanzhangcs.github.io/tamm-page.
Recent advancements in the study of Neural Radiance Fields (NeRF) for dynamic scenes often involve explicit modeling of scene dynamics. However this approach faces challenges in modeling scene dynamics in urban environments where moving objects of various categories and scales are present. In such settings it becomes crucial to effectively eliminate moving objects to accurately reconstruct static backgrounds. Our research introduces an innovative method termed here as Entity-NeRF which combines the strengths of knowledge-based and statistical strategies. This approach utilizes entity-wise statistics leveraging entity segmentation and stationary entity classification through thing/stuff segmentation. To assess our methodology we created an urban scene dataset masked with moving objects. Our comprehensive experiments demonstrate that Entity-NeRF notably outperforms existing techniques in removing moving objects and reconstructing static urban backgrounds both quantitatively and qualitatively.
Scaling up model and data size has been quite successful for the evolution of LLMs. However the scaling law for the diffusion based text-to-image (T2I) models is not fully explored. It is also unclear how to efficiently scale the model for better performance at reduced cost. The different training settings and expensive training cost make a fair model comparison extremely difficult. In this work we empirically study the scaling properties of diffusion based T2I models by performing extensive and rigours ablations on scaling both denoising backbones and training set including training scaled UNet and Transformer variants ranging from 0.4B to 4B parameters on datasets upto 600M images. For model scaling we find the location and amount of cross attention distinguishes the performance of existing UNet designs. And increasing the transformer blocks is more parameter-efficient for improving text-image alignment than increasing channel numbers. We then identify an efficient UNet variant which is 45% smaller and 28% faster than SDXL's UNet. On the data scaling side we show the quality and diversity of the training set matters more than simply dataset size. Increasing caption density and diversity improves text-image alignment performance and the learning efficiency. Finally we provide scaling functions to predict the text-image alignment performance as functions of the scale of model size compute and dataset size.
Dataset distillation has emerged as a promising approach in deep learning enabling efficient training with small synthetic datasets derived from larger real ones. Particularly distribution matching-based distillation methods attract attention thanks to its effectiveness and low computational cost. However these methods face two primary limitations: the dispersed feature distribution within the same class in synthetic datasets reducing class discrimination and an exclusive focus on mean feature consistency lacking precision and comprehensiveness. To address these challenges we introduce two novel constraints: a class centralization constraint and a covariance matching constraint. The class centralization constraint aims to enhance class discrimination by more closely clustering samples within classes. The covariance matching constraint seeks to achieve more accurate feature distribution matching between real and synthetic datasets through local feature covariance matrices particularly beneficial when sample sizes are much smaller than the number of features. Experiments demonstrate notable improvements with these constraints yielding performance boosts of up to 6.6% on CIFAR10 2.9% on SVHN 2.5% on CIFAR100 and 2.5% on TinyImageNet compared to the state-of-the-art relevant methods. In addition our method maintains robust performance in cross-architecture settings with a maximum performance drop of 1.7% on four architectures. Code is available at https://github.com/VincenDen/IID.
Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information. Therefore we propose UnSAMFlow an unsupervised flow network that also leverages object information from the latest foundation model Segment Anything Model (SAM). We first include a self-supervised semantic augmentation module tailored to SAM masks. We also analyze the poor gradient landscapes of traditional smoothness losses and propose a new smoothness definition based on homography instead. A simple yet effective mask feature module has also been added to further aggregate features on the object level. With all these adaptations our method produces clear optical flow estimation with sharp boundaries around objects which outperforms state-of-the-art methods on both KITTI and Sintel datasets. Our method also generalizes well across domains and runs very efficiently.
The goal of multi-task learning is to learn diverse tasks within a single unified network. As each task has its own unique objective function conflicts emerge during training resulting in negative transfer among them. Earlier research identified these conflicting gradients in shared parameters between tasks and attempted to realign them in the same direction. However we prove that such optimization strategies lead to sub-optimal Pareto solutions due to their inability to accurately determine the individual contributions of each parameter across various tasks. In this paper we propose the concept of task priority to evaluate parameter contributions across different tasks. To learn task priority we identify the type of connections related to links between parameters influenced by task-specific losses during backpropagation. The strength of connections is gauged by the magnitude of parameters to determine task priority. Based on these we present a new method named connection strength-based optimization for multi-task learning which consists of two phases. The first phase learns the task priority within the network while the second phase modifies the gradients while upholding this priority. This ultimately leads to finding new Pareto optimal solutions for multiple tasks. Through extensive experiments we show that our approach greatly enhances multi-task performance in comparison to earlier gradient manipulation methods.
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers
PDF ↗In this paper we introduce VoteCut an innovative method for unsupervised object discovery that leverages feature representations from multiple self-supervised models. VoteCut employs normalized-cut based graph partitioning clustering and a pixel voting approach. Additionally We present CuVLER (Cut-Vote-and-LEaRn) a zero-shot model trained using pseudo-labels generated by VoteCut and a novel soft target loss to refine segmentation accuracy. Through rigorous evaluations across multiple datasets and several unsupervised setups our methods demonstrate significant improvements in comparison to previous state-of-the-art models. Our ablation studies further highlight the contributions of each component revealing the robustness and efficacy of our approach. Collectively VoteCut and CuVLER pave the way for future advancements in image segmentation.
Matching 2D keypoints in an image to a sparse 3D point cloud of the scene without requiring visual descriptors has garnered increased interest due to its low memory requirements inherent privacy preservation and reduced need for expensive 3D model maintenance compared to visual descriptor-based methods. However existing algorithms often compromise on performance resulting in a significant deterioration compared to their descriptor-based counterparts. In this paper we introduce DGC-GNN a novel algorithm that employs a global-to-local Graph Neural Network (GNN) that progressively exploits geometric and color cues to rep- resent keypoints thereby improving matching accuracy. Our procedure encodes both Euclidean and angular relations at a coarse level forming the geometric embedding to guide the point matching. We evaluate DGC-GNN on both indoor and outdoor datasets demonstrating that it not only doubles the accuracy of the state-of-the-art visual descriptor-free algorithm but also substantially narrows the performance gap between descriptor-based and descriptor free methods.
Recent progress in single-image 3D generation highlights the importance of multi-view coherency leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale images. However the aspect of novel-view diversity remains underexplored within the research landscape due to the ambiguity in converting a 2D image into 3D content where numerous potential shapes can emerge. Here we aim to address this research gap by simultaneously addressing both consistency and diversity. Yet striking a balance between these two aspects poses a considerable challenge due to their inherent trade-offs. This work introduces HarmonyView a simple yet effective diffusion sampling technique adept at decomposing two intricate aspects in single-image 3D generation: consistency and diversity. This approach paves the way for a more nuanced exploration of the two critical dimensions within the sampling process. Moreover we propose a new evaluation metric based on CLIP image and text encoders to comprehensively assess the diversity of the generated views which closely aligns with human evaluators' judgments. In experiments HarmonyView achieves a harmonious balance demonstrating a win-win scenario in both consistency and diversity.
We present S4Former a novel approach to training Vision Transformers for Semi-Supervised Semantic Segmentation (S4). At its core S4Former employs a Vision Transformer within a classic teacher-student framework and then leverages three novel technical ingredients: PatchShuffle as a parameter-free perturbation technique Patch-Adaptive Self-Attention (PASA) as a fine-grained feature modulation method and the innovative Negative Class Ranking (NCR) regularization loss. Based on these regularization modules aligned with Transformer-specific characteristics across the image input feature and output dimensions S4Former exploits the Transformer's ability to capture and differentiate consistent global contextual information in unlabeled images. Overall S4Former not only defines a new state of the art in S4 but also maintains a streamlined and scalable architecture. Being readily compatible with existing frameworks S4Former achieves strong improvements (up to 4.9%) on benchmarks like Pascal VOC 2012 COCO and Cityscapes with varying numbers of labeled data. The code is at https://github.com/JoyHuYY1412/S4Former.
Clustering multiple motions from observed point trajectories is a fundamental task in understanding dynamic scenes. Most motion models require multiple tracks to estimate their parameters hence identifying clusters when multiple motions are observed is a very challenging task. This is even aggravated for high-dimensional motion models. The starting point of our work is that this high-dimensionality of motion model can actually be leveraged to our advantage as sufficiently long trajectories identify the underlying motion uniquely in practice. Consequently we propose to learn a mapping from trajectories to embedding vectors that represent the generating motion. The obtained trajectory embeddings are useful for clustering multiple observed motions but are also trained to contain sufficient information to recover the parameters of the underlying motion by utilizing a geometric loss. We therefore are able to use only weak supervision from given motion segmentation to train this mapping. The entire algorithm consisting of trajectory embedding clustering and motion parameter estimation is highly efficient. We conduct experiments on the Hopkins155 Hopkins12 and KT3DMoSeg datasets and show state-of-the-art performance of our proposed method for trajectory-based motion segmentation on full sequences and its competitiveness on the occluded sequences. Project page: https://ylochman.github.io/trajectory-embedding.
In Multimodal Large Language Models (MLLMs) a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs enabling profound visual understanding while harnessing the LLMs' robust capabilities. Despite the importance of the visual projector it has been relatively less explored. In this study we first identify two essential projector properties: (i) flexibility in managing the number of visual tokens crucial for MLLMs' overall efficiency and (ii) preservation of local context from visual features vital for spatial understanding. Based on these findings we propose a novel projector design that is both flexible and locality-enhanced effectively satisfying the two desirable properties. Additionally we present comprehensive strategies to effectively utilize multiple and multifaceted instruction datasets. Through extensive experiments we examine the impact of individual design choices. Finally our proposed MLLM Honeybee remarkably outperforms previous state-of-the-art methods across various benchmarks including MME MMBench SEED-Bench and LLaVA-Bench achieving significantly higher efficiency. Code and models are available at https://github.com/kakaobrain/honeybee.
Document image restoration is a crucial aspect of Document AI systems as the quality of document images significantly influences the overall performance. Prevailing methods address distinct restoration tasks independently leading to intricate systems and the incapability to harness the potential synergies of multi-task learning. To overcome this challenge we propose DocRes a generalist model that unifies five document image restoration tasks including dewarping deshadowing appearance enhancement deblurring and binarization. To instruct DocRes to perform various restoration tasks we propose a novel visual prompt approach called Dynamic Task-Specific Prompt (DTSPrompt). The DTSPrompt for different tasks comprises distinct prior features which are additional characteristics extracted from the input image. Beyond its role as a cue for task-specific execution DTSPrompt can also serve as supplementary information to enhance the model's performance. Moreover DTSPrompt is more flexible than prior visual prompt approaches as it can be seamlessly applied and adapted to inputs with high and variable resolutions. Experimental results demonstrate that DocRes achieves competitive or superior performance compared to existing state-of-the-art task-specific models. This underscores the potential of DocRes across a broader spectrum of document image restoration tasks. The source code is publicly available at https://github.com/ZZZHANG-jx/DocRes.
Real-world objects and environments are predominantly composed of edge features including straight lines and curves. Such edges are crucial elements for various applications such as CAD modeling surface meshing lane mapping etc. However existing traditional methods only prioritize lines over curves for simplicity in geometric modeling. To this end we introduce EMAP a new method for learning 3D edge representations with a focus on both lines and curves. Our method implicitly encodes 3D edge distance and direction in Unsigned Distance Functions (UDF) from multi-view edge maps. On top of this neural representation we propose an edge extraction algorithm that robustly abstracts parametric 3D edges from the inferred edge points and their directions. Comprehensive evaluations demonstrate that our method achieves better 3D edge reconstruction on multiple challenging datasets. We further show that our learned UDF field enhances neural surface reconstruction by capturing more details.
Event cameras capture the world at high time resolution and with minimal bandwidth requirements. However event streams which only encode changes in brightness do not contain sufficient scene information to support a wide variety of downstream tasks. In this work we design generalized event cameras that inherently preserve scene intensity in a bandwidth-efficient manner. We generalize event cameras in terms of when an event is generated and what information is transmitted. To implement our designs we turn to single-photon sensors that provide digital access to individual photon detections; this modality gives us the flexibility to realize a rich space of generalized event cameras. Our single-photon event cameras are capable of high-speed high-fidelity imaging at low readout rates. Consequently these event cameras can support plug-and-play downstream inference without capturing new event datasets or designing specialized event-vision models. As a practical implication our designs which involve lightweight and near-sensor-compatible computations provide a way to use single-photon sensors without exorbitant bandwidth costs.
Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. However existing methods often compromise the model performance or require additional training which is undesirable for operators and users. To address this issue we propose Gaussian Shading a diffusion model watermarking technique that is both performance-lossless and training-free while serving the dual purpose of copyright protection and tracing of offending content. Our watermark embedding is free of model parameter modifications and thus is plug-and-play. We map the watermark to latent representations following a standard Gaussian distribution which is indistinguishable from latent representations obtained from the non-watermarked diffusion model. Therefore we can achieve watermark embedding with lossless performance for which we also provide theoretical proof. Furthermore since the watermark is intricately linked with image semantics it exhibits resilience to lossy processing and erasure attempts. The watermark can be extracted by Denoising Diffusion Implicit Models (DDIM) inversion and inverse sampling. We evaluate Gaussian Shading on multiple versions of Stable Diffusion and the results demonstrate that Gaussian Shading not only is performance-lossless but also outperforms existing methods in terms of robustness.
Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. Existing methods learn representations based on trajectory labels for individuals which are annotation-expensive. To provide a more realistic reflection of the underlying practical challenge we introduce a weakly supervised VIC task wherein trajectory labels are not provided. Instead two types of labels are provided to indicate traffic entering the field of view (inflow) and leaving the field view (outflow). We also propose the first solution as a baseline that formulates the task as a weakly supervised contrastive learning problem under group-level matching. In doing so we devise an end-to-end trainable soft contrastive loss to drive the network to distinguish inflow outflow and the remaining. To facilitate future study in this direction we generate annotations from the existing VIC datasets SenseCrowd and CroHD and also build a new dataset UAVVIC. Extensive results show that our baseline weakly supervised method outperforms supervised methods and thus little information is lost in the transition to the more practically relevant weakly supervised task. The code and trained model can be found at CGNet.
Human perception of the world is shaped by a multitude of viewpoints and modalities. While many existing datasets focus on scene understanding from a certain perspective (e.g. egocentric or third-person views) our dataset offers a panoptic perspective (i.e. multiple viewpoints with multiple data modalities). Specifically we encapsulate third-person panoramic and front views as well as egocentric monocular/binocular views with rich modalities including video multi-channel audio directional binaural delay location data and textual scene descriptions within each scene captured presenting comprehensive observation of the world. To the best of our knowledge this is the first database that covers multiple viewpoints with multiple data modalities to mimic how daily information is accessed in the real world. Through our benchmark analysis we presented 5 different scene understanding tasks on the proposed 360+x dataset to evaluate the impact and benefit of each data modality and perspective in panoptic scene understanding. We hope this unique dataset could broaden the scope of comprehensive scene understanding and encourage the community to approach these problems from more diverse perspectives.