Monocular human motion capture in occlusion scenarios presents significant challenges. Although a few works have explicitly considered the occlusion problem, image-based methods are unreliable due to the lack of temporal constraints while video-based approaches cannot gain sufficient knowledge from time domain motion priors to address long-term occlusions. However, occluded human motion typically exhibits periodic patterns and consistent momentum. Inspired by this observation, we exploit reliable image observations in frequency domain and formulate the motion capture task as a wavelet coefficients selection process. Specifically, we first construct probabilistic distributions for the occluded 2D keypoints, and then introduce a frequency domain diffusion model to refine the distributions by learning long-term periodic information and physical momentum with Discrete Wavelet Transform (DWT). Consequently, the learned denoising prior can select valid wavelet components to facilitate the 3D motion capture with a 3D decoder. By employing a joint reprojection strategy, we can also use the same diffusion process to train the 3D decoder. To further promote human occlusion-related tasks, we also present the first 3D occluded motion dataset, OcMotion, which serves as a new benchmark for both training and evaluation. Experimental results demonstrate that our method can produce accurate and coherent human motions from occluded videos. More information is available at https://github.com/boycehbz/FreqMotion.
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Deciphering Genotype-Phenotype Mechanisms from High-Content Profiling via Knowledge-Guided Multi-modal Graph Learning
PDF ↗Understanding genotype-phenotype relationships is pivotal for advancing biomedical research, drug discovery, and precision medicine. With the rise of high-throughput cellular imaging, it is essential to tightly integrate high-content cellular morphology with structured biological knowledge to extract cellular-scale evidence for genotype-to-phenotype mapping.However, integrating high-dimensional, heterogeneous, and noisy phenotypes with structured knowledge remains challenging. Prior approaches typically treat phenotypes as node features, overlooking that phenotypes primarily convey cellular-scale relational signals about how perturbations reshape interactions. We present KERNEL, a knowledge-guided multimodal graph learning framework that integrates cellular imaging phenotypes into a unified knowledge graph to predict genotype-phenotype interactions, including GRN inference, drug-target interaction prediction, and subtype-specific subnetwork discovery. KERNEL dynamically augments task-relevant edges from noisy phenotypic signals, explicitly learns per-edge confidence and marginal utility, and uses knowledge gating to align graph topology with mechanistic pathways. Across large-scale imaging and single-cell datasets, KERNEL consistently outperforms state-of-the-art baselines, e.g., up to 38.1% AUPR improvement for GRN inference, while delivering more accurate and interpretable DTI and subtype subnetwork discovery, demonstrating robust mechanism learning from richer, harder-to-denoise phenotypes.
Understanding region-wise correspondences between manga line art images is fundamental for high-level manga processing, supporting downstream tasks such as line art colorization and in-between frame generation. Unlike natural images that contain rich visual cues, manga line art consists only of sparse black-and-white strokes, making it challenging to determine which regions correspond across images. In this work, we introduce a new task: predicting region-wise correspondence between raw manga line art images without any annotations. To address this problem, we propose a Transformer-based framework trained on large-scale, automatically generated region correspondences. The model learns to suppress noisy matches and strengthen consistent structural relationships, resulting in robust patch-level feature alignment within and across images. During inference, our method segments each line art and establishes coherent region-level correspondences through edge-aware clustering and region matching. We construct manually annotated benchmarks for evaluation, and experiments across multiple datasets demonstrate both high patch-level accuracy and strong region-level correspondence performance, achieving 78.4-84.4% region-level accuracy. These results highlight the potential of our method for real-world manga and animation applications.
FLOW: Optimal Transport-Driven Feature Warping for Generalized Remote Physiological Measurement
PDF ↗Remote photoplethysmography (rPPG) enables non-contact physiological measurement from facial videos but often suffers from severe performance degradation under domain shifts. Traditional STMap-based methods [??] rely on predefined spatio-temporal representations that offer engineered robustness but discard fine-grained temporal cues. In contrast, end-to-end rPPG models directly learn hierarchical features from raw videos, capturing richer physiological patterns yet remaining highly sensitive to variations in illumination, motion, and camera sensors. To address these challenges, we propose FLOW (Feature-Level Optimal Warping), an Optimal Transport (OT)-driven and plug-and-play framework for multi-source domain generalization in rPPG measurement.FLOW formulates domain shifts as structured Optimal Transport problems and performs feature-level warping to align multiple source domains in a shared latent space. Specifically, a dual-consistency regularization is proposed to enforce both frequency fidelity and mapping invariance, while a shape-adaptive alignment module is designed to enable architecture-agnostic integration without re-training. We further derive a generalization bound based on conditional OT discrepancy, providing theoretical insight into FLOW's robustness under distributional shifts. Extensive experiments across diverse rPPG benchmarks demonstrate that FLOW consistently improves cross-domain generalization while maintaining lightweight and modular deployment.
Event cameras provide microsecond latency, making them suitable for 6D object pose tracking in fast, dynamic scenes where conventional RGB and depth pipelines suffer from motion blur and large pixel displacements. We introduce EventTrack6D, an event-depth tracking framework that generalizes to novel objects without object-specific training by reconstructing both intensity and depth at arbitrary timestamps between depth frames. Conditioned on the most recent depth measurement, our dual reconstruction recovers dense photometric and geometric cues from sparse event streams. Our EventTrack6D operates at over 120 FPS and maintains temporal consistency under rapid motion. To support training and evaluation, we introduce a comprehensive benchmark suite: a large-scale synthetic dataset for training and two complementary evaluation sets, including real and simulated event datasets. Trained exclusively on synthetic data, EventTrack6D generalizes effectively to real-world scenarios without fine-tuning, maintaining accurate tracking across diverse objects and motion patterns. Our method and datasets validate the effectiveness of event cameras for event-based 6D pose tracking of novel objects. Code and datasets are publicly available at https://chohoonhee.github.io/Event6D.
Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer continuous risk maps. Existing methods lack the causal reasoning and multimodal understanding required for reliable generalization. We introduce FireScope-Bench, a large-scale dataset and benchmark that couples Sentinel-2 imagery and climate data with expert-defined risk rasters across the USA, and real wildfire events in Europe for cross-continental evaluation. Building on this dataset, we propose FireScope, a VLM-based reasoning-to-generation framework that learns from both reinforcement learning and visual supervision to predict risk rasters with complementary reasoning traces. When trained in the USA and tested in Europe, FireScope achieves substantial performance gains, while expert feedback and automated analysis confirm that its reasoning traces are faithful and semantically meaningful. Our findings demonstrate that reasoning can ground raster prediction models, improving both generalization and interpretability. To our knowledge, this is the first framework to (1) demonstrate that language-based reasoning can improve generalization in visual generation, (2) propose a high-resolution wildfire risk model that can be applied across continents, and (3) enable systematic studies of robust cross-continental generalization for multimodal fire risk models. We believe that FireScope-Bench has the potential to serve as a foundation for advancing reasoning-driven, interpretable and generalizable spatial modeling. Data and source code are available at github.com/insait-institute/FireScope.
Camera and object motions are central to a video's narrative. However, precisely editing these captured motions remains a significant challenge, especially under complex object movements. Current motion-controlled image-to-video (I2V) approaches often lack full-scene context for consistent video editing, while video-to-video (V2V) methods provide viewpoint changes or basic object translation, but offer limited control over fine-grained object motion. We present a track-conditioned V2V framework that enables joint editing of camera and object motion. We achieve this by conditioning a video generation model on a source video and paired 3D point tracks representing source and target motions. These 3D tracks establish sparse correspondences that transfer rich context from the source video to new motions while preserving spatiotemporal coherence. Crucially, compared to 2D tracks, 3D tracks provide explicit depth cues, allowing the model to resolve depth order and handle occlusions for precise motion editing. Trained in two stages on synthetic and real data, our model supports diverse motion edits, including joint camera/object manipulation, motion transfer, and non-rigid deformation, unlocking new creative potential in video editing.
SAIDO: Generalizable Detection of AI-Generated Images via Scene-Aware and Importance-Guided Dynamic Optimization in Continual Learning
PDF ↗The widespread misuse of image generation technologies has raised security concerns, driving the development of AI-generated image detection methods. However, generalization has become a key challenge and open problem: existing approaches struggle to adapt to emerging generative methods and content types in real-world scenarios. To address this issue, we propose a Scene-Aware and Importance-Guided Dynamic Optimization detection framework with continual learning (SAIDO). Specifically, we design Scene-Awareness-Based Expert Module (SAEM) that dynamically identifies and incorporates new scenes using VLLMs. For each scene, independent expert modules are dynamically allocated, enabling the framework to capture scene-specific forgery features better and enhance cross-scene generalization. To achieve an effective balance between model plasticity and stability when learning from multiple image generative methods, we introduce Importance-Guided Dynamic Optimization Mechanism (IDOM), which optimizes each neuron through an importance-guided gradient projection strategy, thereby achieving an effective balance between model plasticity and stability. Extensive experiments on continual learning tasks demonstrate that our method outperforms the current SOTA method in both stability and plasticity, achieving 44.22% and 40.57% relative reductions in average detection error rate and forgetting rate, respectively. On open-world datasets, it improves the average detection accuracy by 9.47% compared to the current SOTA method.
Open-Vocabulary Human-Object Interaction (OV-HOI) detection aims to recognize novel HOI categories beyond the training set. Existing OV-HOI detection approaches typically leverage CLIP to extract global visual representations and perform cross-attention between learnable queries and global features to localize human-object pairs. However, such one-stage paradigms tend to overfit seen interactions, limiting their generalization to unseen categories, while the coarse spatial awareness of CLIP also hinders the localization of fine-grained interaction cues. To address these issues, we propose a novel Semantic-Diversified and Interaction-Focused framework (SD-IF), which integrates reinforcement-guided adaptive optimization to jointly enhance semantic generalization and spatial discrimination. Specifically, we introduce a Semantic Diversification (SD) module that applies reinforcement-driven stochastic semantic perturbations and dual-level semantic exploration, expanding the semantic coverage of queries while maintaining visual coherence and effectively encouraging exploration beyond the seen semantic clusters. Furthermore, we design an Interaction Focusing (IF) module that formulates an actor-critic optimization scheme to adaptively refine attention distributions based on detection features and interaction representations, guided by a hybrid reward combining spatial focusing and semantic consistency. Extensive experiments on two standard benchmarks demonstrate the effectiveness of our SD-IF.
RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation
PDF ↗We introduce RAVEN, a deep learning architecture for processing frequency-modulated continuous-wave (FMCW) radar data that is designed for high computational efficiency. RAVEN reduces computation by using a learnable antenna mixer module on independent receiver state space encoders (SSM) to compress the virtual MIMO array into a compact set of learned features and by performing per-chirp inference with a calibrated early-exit rule, so the model reaches a decision using only a subset of chirps in a radar frame. These design choices yield up to 170x lower computation and 4x lower end-to-end latency than conventional frame-based radar backbones, while achieving state-of-the-art detection and BEV free-space segmentation performance on automotive radar datasets.
We introduce ART, Articulated Reconstruction Transformer--a category-agnostic, feed-forward model that reconstructs complete 3D articulated objects from only sparse, multi-state RGB images. Previous methods for articulated object reconstruction either rely on slow optimization with fragile cross-state correspondences or use feed-forward models limited to specific object categories. In contrast, ART treats articulated objects as assemblies of rigid parts, formulating reconstruction as a part-based prediction problem. Our newly designed transformer architecture maps sparse image inputs to a set of learnable part slots, from which ART jointly decodes unified representations for individual parts, including their 3D geometry, texture, and explicit articulation parameters. The resulting reconstructions are physically interpretable and readily exportable to standard simulation formats. Trained on a large-scale, diverse dataset with per-part supervision, and evaluated across diverse benchmarks, ART achieves significant improvements over existing baselines and establishes a new state of the art for articulated object reconstruction from image inputs.
Training-Free Open-Vocabulary Camouflaged Object Segmentation via Fine-Grained Object Binding and Adaptive Hybrid Prompt
PDF ↗Vision-Language models (e.g., CLIP) facilitate the development of open-vocabulary camouflaged object segmentation (OVCOS), but existing methods still rely on mask annotations for fully-supervised training. In contrast, the training-free paradigm can rapidly process unseen data, representing a highly promising solution. However, in camouflage scenarios, existing training-free methods utilize sparse textual prompts and ignore the category similarity between visual patches, leading to inadequate object binding capability. To alleviate these issues, we propose a fine-grained object binding and adaptive hybrid prompt framework for training-free OVCOS. The framework first employs multimodal large language models (MLLMs) to explicitly model fine-grained textual descriptions of camouflaged objects and background. Building on this, we construct a semantic probe to decouple object and background features and explicitly model category similarity between visual patches via semantic consistency ranking, thereby achieving accurate object binding. Subsequently, we propose an entropy-guided text embedding adjustment strategy to adjust textual embeddings, aiming to further enhance fine-grained object binding. Finally, we utilize an adaptive hybrid prompt generation strategy to generate hybrid prompts, assisting SAM in accurately segmenting camouflaged objects. Experimental results on the OVCamo benchmark demonstrate that our method achieves excellent performance, significantly surpassing the advanced training-free ResCLIP.
High-quality 4D reconstruction enables photorealistic and immersive rendering of the dynamic real world. However, unlike static scenes that can be fully captured with a single camera, high-quality dynamic scenes typically require dense arrays of tens or even hundreds of synchronized cameras. Dependence on such costly lab setups severely limits practical scalability. The reliance on such costly lab setups severely limits practical scalability. To this end, we propose a sparse-camera dynamic reconstruction framework that exploits abundant yet inconsistent generative observations. Our key innovation is the Spatio-Temporal Distortion Field, which provides a unified mechanism for modeling inconsistencies in generative observations across both spatial and temporal dimensions. Building on this, we develop a complete pipeline that enables 4D reconstruction from sparse and uncalibrated camera inputs. We evaluate our method on multi-camera dynamic scene benchmarks, achieving spatio-temporally consistent high-fidelity renderings and significantly outperforming existing approaches.
Recent work explores how real and synthetic data contribute to VLA model generalization. While the \pi-series model has shown the strong effectiveness of large-scale real-robot pre-training, synthetic data has not previously demonstrated comparable capability at scale.This paper provides the first evidence that synthetic data alone can match the performance of the strongest \pi-dataset in pre-training a VLA model, revealing the substantial value of large-scale simulation.The resulting model also exhibits surprisingly strong zero-shot sim-to-real transfer on several challenging tasks.Our synthetic dataset, InternData-A1, contains over 630k trajectories and 7,433 hours across 4 embodiments, 18 skills, 70 tasks, and 227 scenes, covering rigid, articulated, deformable, and fluid-object manipulation. It is generated through a highly autonomous, fully decoupled, and compositional simulation pipeline that enables flexible task assembly, long-horizon skill composition, and heterogeneous embodiments with minimal manual tuning.Using the same architecture as \pi_0, we pre-train a model entirely on InternData-A1 and find that it matches the official \pi_0 across 49 simulation tasks, 5 real-world tasks, and 4 long-horizon dexterous tasks.We will open-source both the dataset and the generation pipeline to broaden access to large-scale robotic data and to lower the barrier to scalable data creation for embodied AI research.
Generalization remains a critical challenge in deep learning-based point cloud geometry compression. While existing methods perform well on standard benchmarks, their performance collapses in real-world scenarios due to two fundamental limitations: the lack of context models that are robust across diverse data densities, and the inability to efficiently adapt to out-of-distribution (OOD) data. To overcome both challenges, we introduce AnyPcc, a universal point cloud compression framework. AnyPcc first employs a Universal Context Model that leverages coarse-grained spatial priors with fine-grained channel priors to ensure robust context modeling across the entire density spectrum. Second, our novel Instance-Adaptive Fine-Tuning (IAFT) strategy tackles OOD data by synergizing explicit and implicit compression paradigms. For each instance, it fine-tunes a small subset of network weights and transmits them within the bitstream. The minimal bitrate overhead from these weights is significantly outweighed by the resulting gains in geometry compression. Extensive experiments on a benchmark of 15 diverse datasets confirm that AnyPcc sets a new state-of-the-art in point cloud compression while maintaining low complexity. Our code and datasets have been released to encourage reproducible research.
Proxy-GS: Unified Occlusion Priors for Training and Inference in Structured 3D Gaussian Splatting
PDF ↗3D Gaussian Splatting (3DGS) has emerged as an efficient approach for photorealistic rendering. Recent MLP-based variants further improve visual fidelity but introduce substantial decoding overhead during rendering. To reduce the computational cost, several pruning strategies and level-of-detail (LOD) techniques have been introduced to reduce the number of Gaussian primitives in large-scale scenes. However, our analysis reveals that significant redundancy still remains due to the lack of occlusion awareness. In this work, we propose Proxy-GS, a novel pipeline that uses a proxy representation to introduce occlusion awareness for Gaussians from arbitrary views. At the core of our approach is a fast proxy system capable of producing precise occlusion depth maps at a resolution of 1000 x 1000 in under 1 ms. This proxy serves two roles. First, it guides the culling of anchors and Gaussians to accelerate rendering. Second, it guides densification toward scene surfaces during training, reducing inconsistencies in occluded regions and thereby improving rendering quality. In heavily occluded scenarios such as the MatrixCity Streets dataset, Proxy-GS achieves more than a 2.5x speedup over Octree-GS while also improving rendering quality.
Multi-modal large language models (MLLMs) have shown remarkable progress in integrating visual and linguistic understanding. Recent efforts have extended these capabilities to 3D understanding through encoder-based architectures that rely on pre-trained 3D encoders to extract geometric features. However, such approaches suffer from semantic misalignment between geometric and linguistic spaces, resolution sensitivity, and substantial computational overhead. In this work, we present SAGE, the first end-to-end 3D MLLM that directly processes raw point clouds without relying on a pre-trained 3D encoder. Our approach introduces a lightweight 3D tokenizer that combines geometric sampling and neighbourhood aggregation with vector quantization to convert point clouds into discrete tokens--treating 3D data as a foreign language that naturally extends the LLM's vocabulary. Furthermore, to enhance the model's reasoning capability on complex 3D tasks, we propose a preference optimization training strategy with a semantic alignment-based reward, specifically designed for open-ended 3D question answering where responses are descriptive. Extensive experiments across diverse 3D understanding benchmarks demonstrate that our end-to-end approach outperforms existing encoder-based methods while offering significant advantages in computational efficiency, generalization across LLM backbones, and robustness to input resolution variations. Code is available at: https://github.com/snehaputul/SAGE3D.
Gait recognition is a biometric technique that identifies individuals based on their walking patterns, offering advantages in long-range, non-intrusive scenarios. However, real-world scenarios often involve heterogeneous sensing modalities such as LiDAR and RGB cameras, making LiDAR-Camera Cross-modal Gait recognition (LCCGR) a critical yet challenging task due to the substantial modality gap between 2D videos and 3D point cloud sequences. To address this challenge, we propose TCFDNet, a Text-guided Cross-modal Feature Disentanglement Network, which leverages modality-aware textual priors as semantic anchors to guide the learning of disentangled modality-shared representations. Specifically, we construct a Gait Modality Text Dictionary (GMTD) using large language models to generate rich semantic descriptions of gait across modalities and viewpoints. A CLIP-based Multi-grained Feature Encoder then aligns visual and textual features within a unified vision-language space. Furthermore, the Text-guided Feature Disentanglement (TFD) module selects the top\text - k matched textual descriptions to reconstruct modality-specific representations and derive modality-shared features via residual decomposition and orthogonality constraints. To mitigate the fragility of the disentangled shared features, we propose a Feature Stability Enhancement (FSE) module, which models spatial and channel-wise correlations to improve feature robustness. In addition, a cross-modal patch exchange strategy is introduced to further improve generalization. Extensive experiments on SUSTech1K and FreeGait datasets demonstrate that TCFDNet achieves new state-of-the-art results and validate the effectiveness of the proposed modules.
While recent text-to-video (T2V) diffusion models have achieved impressive quality and prompt alignment, they often produce low-diversity outputs when sampling multiple videos from a single text prompt. We tackle this challenge by formulating it as a set-level policy optimization problem, with the goal of training a policy that can cover the diverse range of plausible outcomes for a given prompt. To address this, we introduce DPP-GRPO, a novel framework for diverse video generation generation that combines Determinantal Point Processes (DPPs) and Group Relative Policy Optimization (GRPO) theories to enforce explicit reward on diverse generations. Our objective turns diversity into an explicit signal by imposing diminishing returns on redundant samples (via DPP) while supplies groupwise feedback over candidate sets (via GRPO). Our framework is plug-and-play and model-agnostic, and encourages diverse generations across visual appearance, camera motions, and scene structure without sacrificing prompt fidelity or perceptual quality. We implement our method on WAN and CogVideoX, and show that our method consistently improves video diversity on state-of-the-art benchmarks such as VBench, VideoScore, and human preference studies. Moreover, we release our code and a new benchmark dataset of 30,000 diverse prompts to support future research.
Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos---they process every pixel equally in their vision transformers (ViTs) or LLMs despite significant spatiotemporal redundancy. We introduce AutoGaze, a lightweight module that removes redundant patches before processed by a ViT or an MLLM. Trained with next-token prediction and reinforcement learning, AutoGaze autoregressively selects a minimal set of multi-scale patches that reconstructs the video within a user-specified error threshold, eliminating redundancy while preserving information. Empirically, AutoGaze reduces visual tokens by 4x-100x and accelerates ViTs and MLLMs by up to 19x, scaling MLLMs to 1K-frame 4K-resolution videos and achieving superior results on video benchmarks (e.g., 67.0% on VideoMME). Furthermore, we introduce HLVid: the first high-resolution, long-form video QA benchmark with 5-minute 4K-resolution videos, where an MLLM scaled with AutoGaze improves over the baseline by 10.1% and outperforms the previous best MLLM by 4.5%. Project page: https://autogaze.github.io/.