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Chaohong Guo, Yihan He, Yongwei Nie, Fei Ma, Xuemiao Xu, Chengjiang Long

Video Temporal Grounding (VTG) aims to localize the video segment that corresponds to a natural language query, which requires a comprehensive understanding of complex temporal dynamics. Existing Vision-LMMs typically perceive temporal dynamics via positional encoding, text-based timestamps, or visual frame numbering. However, these approaches exhibit notable limitations: assigning each frame a text-based timestamp token introduces additional computational overhead and leads to sparsity in visual attention, positional encoding struggles to capture absolute temporal information, and visual frame numbering often compromises spatial detail. To address these issues, we propose Temporal to Spatial Gridification (T2SGrid), a novel framework that reformulates video temporal understanding as a spatial understanding task. The core idea of T2SGrid is to process video content in clips rather than individual frames. we employ a overlapping sliding windows mechanism to segment the video into temporal clips. Within each window, frames are arranged chronologically in a row-major order into a composite grid image, effectively transforming temporal sequences into structured 2D layouts. The gridification not only encodes temporal information but also enhances local attention within each grid. Furthermore, T2SGrid enables the use of composite text timestamps to establish global temporal awareness. Experiments on standard VTG benchmarks demonstrate that T2SGrid achieves superior performance.

Yuting Tan, Xilong Cheng, Yunxiao Qin, Zhengnan Li, Jingjing Zhang

Humans develop visual intelligence through perceiving and interacting with their environment--a self-supervised learning process grounded in egocentric experience. Inspired by this, we ask how can artificial systems learn stable object representations from continuous, uncurated first-person videos without relying on manual annotations. This setting poses challenges of separating, recognizing, and persistently tracking objects amid clutter, occlusion, and ego-motion. We propose EgoViT, a unified vision Transformer framework designed to learn stable object representations from unlabeled egocentric video. EgoViT bootstraps this learning process by jointly discovering and stabilizing "proto-objects" through three synergistic mechanisms: (1) Proto-object Learning, which uses intra-frame distillation to form discriminative representations; (2) Depth Regularization, which grounds these representations in geometric structure; and (3) Teacher-Filtered Temporal Consistency, which enforces identity over time. This creates a virtuous cycle where initial object hypotheses are progressively refined into stable, persistent representations. The framework is trained end-to-end on unlabeled first-person videos and exhibits robustness to geometric priors of varied origin and quality. On standard benchmarks, EgoViT achieves +8.0% CorLoc improvement in unsupervised object discovery and +4.8% mIoU improvement in semantic segmentation, demonstrating its potential to lay a foundation for robust visual abstraction in embodied intelligence.

Xinyu Peng, Han Li, Yuyang Huang, Ziyang Zheng, Yaoming Wang, Xin Chen, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong

Existing video frame interpolation (VFI) methods often adopt a frame-centric approach, processing videos as independent short segments (e.g., triplets), which leads to temporal inconsistencies and motion artifacts. To overcome this, we propose a holistic, video-centric paradigm named Local Diffusion Forcing for Video Frame Interpolation (LDF-VFI). Our framework is built upon an auto-regressive diffusion transformer that models the entire video sequence to ensure long-range temporal coherence. To mitigate error accumulation inherent in auto-regressive generation, we introduce a novel skip-concatenate sampling strategy that effectively maintains temporal stability. Furthermore, LDF-VFI incorporates sparse, local attention and tiled VAE encoding, a combination that not only enables efficient processing of long sequences but also allows generalization to arbitrary spatial resolutions (e.g., 4K) at inference without retraining. An enhanced conditional VAE decoder, which leverages multi-scale features from the input video, further improves reconstruction fidelity. Empirically, LDF-VFI achieves state-of-the-art performance on challenging VFI benchmarks, demonstrating superior per-frame quality and temporal consistency, especially in scenes with large motion.

Weidong Tang, Zhiyuan Liang, Xinyan Wan, Chen Zhu, Zhaopan Xu, Pengfei Zhou, Yan Song, Yang You, Wangbo Zhao

Large vision foundation models, such as SAM2, have achieved remarkable performance in video object segmentation and tracking (VOST). However, their effectiveness is hindered by significant computational overhead. While model pruning is a widely used strategy to address this issue, traditional static and input-agnostic pruning approaches fall short in managing the diverse and complex nature of video data effectively. A promising alternative is dynamic networks, yet they often struggle to translate theoretical computational reductions into actual acceleration. Furthermore, both static and dynamic approaches typically focus on visual features of individual frames while neglecting the temporal correlations between them, limiting their performance in handling complex video streams.To address these challenges, we propose Recurrent Dynamic Submodel (RDS), a dynamic architecture that adaptively selects submodel blocks for each frame. Specifically, it has a lightweight Prediction-aware Router (PAR), which leverages both the segmentation mask from the previous frame and the visual features of the current frame to make routing decisions, enabling the submodel to explicitly capture the temporal nature of video data. Additionally, to reduce the cost of adapting the dynamic submodel, we introduce an Importance-aware LoRA (I-LoRA), tuning parameters only in the most critical blocks. Extensive experiments show that our approach achieves an optimal trade-off between performance and speed, requiring minimal trainable parameters and training data.

Minghang Zheng, Zihao Yin, Yi Yang, Yuxin Peng, Yang Liu

Video Temporal Grounding (VTG), the task of localizing video segments from text queries, struggles in open-world settings due to limited dataset scale and semantic diversity, causing performance gaps between common and rare concepts. To overcome these limitations, we introduce OmniVTG, a new large-scale dataset for open-world VTG, coupled with a Self-Correction Chain-of-Thought (CoT) training paradigm designed to enhance the grounding capabilities of Multimodal Large Language Models (MLLMs). Our OmniVTG is constructed via a novel Semantic Coverage Iterative Expansion pipeline, which first identifies gaps in the vocabulary of existing datasets and collects videos that are highly likely to contain these target concepts. For high-quality annotation, we leverage the insight that modern MLLMs excel at dense captioning more than direct grounding and design a caption-centric data engine to prompt MLLMs to generate dense, timestamped descriptions. Beyond the dataset, we observe that simple supervised finetuning (SFT) is insufficient, as a performance gap between rare and common concepts still persists. We find that MLLMs' video understanding ability significantly surpasses their direct grounding ability. Based on this, we propose a Self-Correction Chain-of-Thought (CoT) training paradigm. We train the MLLM to first predict, then use its understanding capabilities to reflect on and refine its own predictions. This capability is instilled via a three-stage pipeline of SFT, CoT finetuning, and reinforcement learning. Extensive experiments show our approach not only excels at open-world grounding in our OmniVTG dataset but also achieves state-of-the-art zero-shot performance on four existing VTG benchmarks. Code is available at https://github.com/oceanflowlab/OmniVTG.

Ziqian Yang, Xianglin Qiu, Xinqiao Zhao, Xiaolei Wang, Quan Zhang, Jimin Xiao

Weakly Supervised Semantic Segmentation (WSSS) typically utilizes Class Activation Maps (CAMs) to provide the pixel-wise localization. However, CAMs tend to activate only the most discriminative regions, leading to suboptimal WSSS performance. Although existing CAM refinement methods leverage pair-wise relations in affinity to expand the activation regions, these affinities derived from Vision Transformer (ViTs) exhibit a smoothing property, neglecting crucial high-frequency relations and failing to accurately refine object boundaries. In this work, we propose the Dual Frequency-Aware framework (DFA) to address this limitation. Specifically, the Low-Frequency-Aware Alignment (LFAA) generates low-frequency-aware affinity that captures salient semantic relations to enhance object interior semantic consistency on CAMs, while the High-Frequency-Aware Rectification (HFAR) module produces high-frequency-aware affinity that models precise relations to preserve object boundary structure on CAMs. By effectively integrating these two complementary affinities, we design a novel Frequency-Guided (FG) CAM Generation based on Optimal Transport theory, which significantly omits the complex refinement process. Extensive experiments demonstrate that our DFA framework achieves state-of-the-art performance on both PASCAL VOC and MS COCO benchmarks. Code will be released.

Yuhuan Yang, Xianwei Zhuang, Yuxuan Cai, Chaofan Ma, Shuai Bai, Jiangchao Yao, Ya Zhang, Junyang Lin, Yanfeng Wang

Recent approaches for segmentation have leveraged pretrained generative models as feature extractors, treating segmentation as a downstream adaptation task via indirect feature retrieval. This implicit use suffers from a fundamental misalignment in representation.It also depends heavily on indirect feature extraction pipelines, which complicate the workflow and limit adaptation.In this paper, we argue that instead of indirect adaptation, segmentation tasks should be trained directly in a generative manner.We identify a key obstacle to this unified formulation: VAE latents of binary masks are sharply distributed, noise robust, and linearly separable, distinct from natural image latents.To bridge this gap, we introduce timesteps sampling strategy for binary masks that emphasizes extreme noise levels for segmentation and moderate noise for image generation, enabling harmonious joint training.We present GenMask, a DiT trains to generate black-and-white segmentation masks as well as colorful images in RGB space under the original generative objective. GenMask preserves the original DiT architecture while removing the need of feature extraction pipelines tailored for segmentation tasks. Empirically, GenMask attains state-of-the-art performance on referring and reasoning segmentation benchmarks and ablations quantify the contribution of each component.

Jiacheng Lu, Hui Ding, Shiyu Zhang, Guoping Huo

Brain tumor MRI segmentation is essential for clinical diagnosis and treatment planning, enabling accurate lesion detection and radiotherapy target delineation. However, tumor lesions occupy only a small fraction of the volumetric space, resulting in severe spatial sparsity, while existing segmentation networks often overlook clinically observed spatial priors of tumor occurrence, leading to redundant feature computation over extensive background regions. To address this issue, we propose PGR-Net (Prior-Guided ROI Reasoning Network) - an explicit ROI-aware framework that incorporates a data-driven spatial prior set to capture the distribution and scale characteristics of tumor lesions, providing global guidance for more stable segmentation. Leveraging these priors, PGR-Net introduces a hierarchical Top-K ROI decision mechanism that progressively selects the most confident lesion candidate regions across encoder layers to improve localization precision. We further develop the WinGS-ROI (Windowed Gaussian-Spatial Decay ROI) module, which uses multi-window Gaussian templates with a spatial decay function to produce center-enhanced guidance maps, thus directing feature learning throughout the network. With these ROI features, a windowed RetNet backbone is adopted to enhance localization reliability. Experiments on BraTS 2019/2023 and MSD Task01 show that PGR-Net consistently outperforms existing approaches while using only 8.64M Params, achieving Dice scores of 89.02%, 91.82%, and 89.67% on the Whole Tumor region. Code is available at https://github.com/CNU-MedAI-Lab/PGR-Net.

Jiahao Nie, Guanqiao Fu, Wenbin An, Yap-Peng Tan, Alex C. Kot, Shijian Lu

Cross-Domain Few-Shot Segmentation aims to segment categories in data-scarce domains conditioned on a few exemplars. Typical methods first establish few-shot capability in a large-scale source domain and then adapt it to target domains. However, due to the limited quantity and diversity of target samples, existing methods still exhibit constrained performance. Moreover, the source-trained model's initially weak few-shot capability in target domains, coupled with substantial domain gaps, severely hinders the effective utilization of target samples and further impedes adaptation. To this end, we propose Multi-view Progressive Adaptation, which progressively adapts few-shot capability to target domains from both data and strategy perspectives. (i) From the data perspective, we introduce Hybrid Progressive Augmentation, which progressively generates more diverse and complex views through cumulative strong augmentations, thereby creating increasingly challenging learning scenarios. (ii) From the strategy perspective, we design Dual-chain Multi-view Prediction, which fully leverages these progressively complex views through sequential and parallel learning paths under extensive supervision. By jointly enforcing prediction consistency across diverse and complex views, MPA achieves both robust and accurate adaptation to target domains. Extensive experiments demonstrate that MPA effectively adapts few-shot capability to target domains, outperforming state-of-the-art methods by a large margin (+7.0%). Our code is available at https://github.com/niejiahao1998/MPA.

Jihun Kim, Hoyong Kwon, Hyeokjun Kweon, Kuk-Jin Yoon

Fully supervised Video Semantic Segmentation (VSS) relies heavily on densely annotated video data, limiting practical applicability. Alternatively, applying pre-trained Image Semantic Segmentation (ISS) models frame-by-frame avoids annotation costs but ignores crucial temporal coherence. Recent foundation models such as SAM2 enable high-quality mask propagation yet remain impractical for direct VSS due to limited semantic understanding and computational overhead. In this paper, we propose DiTTA (Distillation-assisted Test-Time Adaptation), a novel framework that converts an ISS model into a temporally-aware VSS model through efficient test-time adaptation (TTA), without annotated videos. DiTTA distills SAM2's temporal segmentation knowledge into the ISS model during a brief, single-pass initialization phase, complemented by a lightweight temporal fusion module to aggregate cross-frame context. Crucially, DiTTA achieves robust generalization even when adapting with highly limited partial video snippets (e.g., initial 10%), significantly outperforming zero-shot refinement approaches that repeatedly invoke SAM2 during inference. Extensive experiments on VSPW and Cityscapes demonstrate DiTTA's effectiveness, achieving competitive or superior performance relative to fully-supervised VSS methods, thus providing a practical and annotation-free solution for real-world VSS tasks.

Xinlong Li, Di Lin, Shaoyiyi Gao, Yaxuan Liu, Jixian He, Jiaxin Li, Ruonan Liu, Qing Guo, Kairui Yang, Wei Feng

Open-vocabulary part segmentation (OVPS) aims to segment objects into fine-grained parts while generalizing to unseen categories. Existing VLM-based methods face two challenges: (1) object over-segmentation, caused by overly broad semantic activations, and (2) part under-segmentation, resulting from weak fine-grained perception. To address these issues, we propose HOPS, a two-stage framework for hierarchical open-vocabulary part segmentation. HOPS introduces a bidirectional semantic-structural attention fusion mechanism that integrates CLIP's semantic alignment with DINO's structural perception. In the object segmentation stage, the Attention-Aware Filtering Module (AFM) refines cross-modal similarity maps via semantic-structural attention to suppress object over-segmentation. In the part segmentation stage, the Affinity-Guided Enhancement Module (AEM) iteratively propagates part responses to progressively expand activation regions, effectively mitigating part under-segmentation. Experiments on Pascal-Part-116, ADE20K-Part-234, and PartImageNet demonstrate that HOPS achieves state-of-the-art performance with superior generalization. Our code is available at https://github.com/TJU-IDVLab/HOPS.

Woojeong Jin, Seongchan Kim, Jaeho Lee, Seungryong Kim

Referring video object segmentation (RVOS) aims to segment objects in a video described by a natural language expression. However, most existing approaches focus only on the referred object (typically the actor), even when the expression clearly describes an interaction involving multiple objects with distinct roles. In this paper, we introduce Interaction-Aware Referring Video Object Segmentation (InterRVOS), a novel task that focuses on explicit interaction modeling by requiring separate segmentation of actor and target objects.This formulation enables fine-grained understanding of object relationships, as many video events are defined by such interactions rather than individual objects. We present InterRVOS-127K, a large-scale dataset of over 127K automatically annotated expressions with distinct actor-target mask pairs, and propose ReVIOSa, a MLLM-based architecture that introduces interaction-aware special tokens and attention mask loss (AML) to enhance interaction-aware segmentation. We also propose a new evaluation protocol that separately evaluates actor and target segmentation for more accurate role distinction. Comprehensive experiments demonstrate that ReVIOSa outperforms existing baselines on the proposed InterRVOS-127K benchmark, with further analyses validating the necessity and effectiveness of both ReVIOSa and InterRVOS-127K.

Fangzhou Lin, Yuping Wang, Yuliang Guo, Zixun Huang, Xinyu Huang, Haichong Zhang, Kazunori Yamada, Zhengzhong Tu, Liu Ren, Ziming Zhang

Partially Supervised Multi-Task Learning (PS-MTL) aims to leverage knowledge across tasks when annotations are incomplete. Existing approaches, however, have largely focused on the simpler setting of homogeneous, dense prediction tasks, leaving the more realistic challenge of learning from structurally diverse tasks unexplored. To this end, we introduce NexusFlow, a novel, lightweight, and plug-and-play framework effective in both settings. NexusFlow introduces a set of surrogate networks with invertible coupling layers to align the latent feature distributions of tasks, creating a unified representation that enables effective knowledge transfer. The coupling layers are bijective, preserving information while mapping features into a shared canonical space. This invertibility avoids representational collapse and enables alignment across structurally different tasks without reducing expressive capacity.We first evaluate NexusFlow on the core challenge of domain-partitioned autonomous driving, where dense map reconstruction and sparse multi-object tracking are supervised in different geographic regions, creating both structural disparity and a strong domain gap. NexusFlow sets a new state-of-the-art result on nuScenes, outperforming strong partially supervised baselines. To demonstrate generality, we further test NexusFlow on NYUv2 using three homogeneous dense prediction tasks, segmentation, depth, and surface normals, as a representative N-task PS-MTL scenario. NexusFlow yields consistent gains across all tasks, confirming its broad applicability.

Yinuo Wang, Yanbo Fan, Xuan Wang, Boyao Zhou, Yu Guo, Yujun Shen, Fei Wang

Dyadic interactive head generation aims to synthesize realistic head motions that respond both verbally and non-verbally to an interlocutor in real-time conversation. The existing works often focus on offline scenarios, and struggle with a shallow understanding of the multimodal conversational context while also lacking long-term coherence. To address these limitations, we propose MimicTalker, a novel method for producing real-time, contextually-aware, and long-term consistent interactive head motions. To this end, we propose a Multimodal Interactive Context Extraction (MICE) module to capture both instantaneous and long-term multimodal interactive information from the interlocutor. To enhance in-depth conversational understanding, we propose a Semantic-enhanced Dynamic Interaction (SDI) module to integrate the intentions and topics of the conversation, which are automatically extracted through an LLM-based analyzer. Further, we propose a semantic-guided Motion Style Memory (MSM) mechanism, enabling the long-term motion consistency throughout the conversation. We conduct experiments on both short conversational segments (25 seconds) and extended dialogues (6 minutes), and the comprehensive experiments demonstrate that our method significantly outperforms existing approaches.

Pei An, Junfeng Ding, Jiaqi Yang, Yulong Wang, Jie Ma, Liangliang Nan

Image-to-point-cloud (I2P) registration aims to align 2D images with 3D point clouds by establishing reliable 2D-3D correspondences. The drastic modality gap between images and point clouds makes it challenging to learn features that are both discriminative and generalizable, leading to severe performance drops in unseen scenarios. We address this challenge by introducing a heterogeneous graph that enables refining both cross-modal features and correspondences within a unified architecture. The proposed graph represents a mapping between segmented 2D and 3D regions, which enhances cross-modal feature interaction and thus improves feature discriminability. In addition, modeling the consistency among vertices and edges within the graph enables pruning of unreliable correspondences. Building on these insights, we propose a heterogeneous graph embedded I2P registration method, termed Hg-I2P. It learns a heterogeneous graph by mining multi-path feature relationships, adapts features under the guidance of heterogeneous edges, and prunes correspondences using graph-based projection consistency. Experiments on six indoor and outdoor benchmarks under cross-domain setups demonstrate that Hg-I2P significantly outperforms existing methods in both generalization and accuracy. Code is released on https://github.com/anpei96/hg-i2p-demo.

Junjie Chen, Junwei Lin, Ren Hong, Shengjie Liu, Yuming Fang, Feng Qian, Yifan Zuo

Amodal instance segmentation aims to segment both visible and occluded regions of object instance, which are challenging due to lacking inference support under occlusion. Most existing methods employ the prior knowledge about object mask (shape prior) to support the amodal estimation, but the shape prior is not always compatible for object instances in the test stage. In this paper, we explore the task of interactive amodal segmentation, where a few user clicks are available for better segmenting the complete masks of object instances. For this task, we propose a novel framework based on learning and aligning click-aware shape prior (termed ClickPriorNet). Specifically, we propose to learn click-aware shape prior with triplet loss, which forces the retrieved shape priors to have higher IoU with the ground-truth of target instance and thus could exactly facilitate the prediction. Besides, considering the inevitable mismatch between shape prior and target instance, we propose to adaptively align the shape prior with deformable attention. Overall, our model could make full use of the interactive clicks to retrieve and align shape priors, and thus could estimate more complete masks. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method. Code is available at https://github.com/chenbys/ClickPriorNet.

Changzhou Han, Wanlun Ma, Xi Tang, Kun Hu, Sheng Wen, Yang Xiang

Sign Language Translation (SLT) converts continuous sign videos into spoken language text, yet current models, whether gloss-based or gloss-free, struggle with long or discourse-level inputs. Recent architectures such as TwoStreamNetwork and CV-SLT have nearly saturated short-sentence accuracy, but their performance degrades on long sentences and multi-sentence paragraphs. In real scenarios such as news, interviews or daily conversations, signers naturally produce extended signing sequences with complex contextual dependencies. Moreover, identifying precise gloss boundaries remains a key obstacle, while gloss-based methods, though often superior, incur heavy annotation costs. The community therefore needs a solution that mitigates gloss dependency while preserving translation quality. We present BoostSLT, a context-aware framework for enhancing semantic consistency over long sign sequences without gloss supervision. Instead of requiring explicit gloss segmentation, BoostSLT introduces an Energy-Aware Temporal Segmentation (EAT-Seg) module that dynamically partitions videos into semantically coherent fragments, followed by a Diffusion-based Semantic Reconstruction (DSR) module that stitches and refines fragment-level translations into globally fluent paragraphs. The framework is plug-and-play and model-agnostic, seamlessly integrating with existing gloss-based or gloss-free pipelines across languages. Experiments on PHOENIX-2014T, CSL-Daily, and Auslan-Daily show consistent BLEU and ROUGE-L gains, confirming that diffusion-driven semantic reconstruction effectively bridges local accuracy and global coherence in long-form SLT. Code is available at github.com/K1sna/BoostSLT.

Nikolay Kormushev, Josip Šarić, Matej Kristan

Open-vocabulary panoptic segmentation remains hindered by two coupled issues: (i) mask selection bias, where objectness heads trained on closed vocabularies suppress masks of categories not observed in training, and (ii) limited regional understanding in vision-language models such as CLIP, which were optimized for global image classification rather than localized segmentation. We introduce OVRCOAT, a simple, modular framework that tackles both. First, a CLIP-conditioned objectness adjustment (COAT) updates background/foreground probabilities, preserving high-quality masks for out-of-vocabulary objects. Second, an open-vocabulary mask-to-text refinement (OVR) strengthens CLIP's region-level alignment to improve classification of both seen and unseen classes with markedly lower memory cost than prior fine-tuning schemes. The two components combine to jointly improve objectness estimation and mask recognition, yielding consistent panoptic gains. Despite its simplicity, OVRCOAT sets a new state of the art on ADE20K (+5.5% PQ) and delivers clear gains on Mapillary Vistas and Cityscapes (+7.1% and +3% PQ, respectively). The code is available here.

Hengzhi Chen, Liqian Feng, Wenhua Wu, Xiaogang Zhu, Qiuxia Wu, Lianlei Shan, Kun Hu

Semantic segmentation of ultra-high-resolution (UHR) remote sensing imagery is critical for applications like environmental monitoring and urban planning but faces com- putational and optimization challenges. Conventional methods either lose fine details through downsampling or fragment global context via patch processing. While multi-branch networks ad- dress this trade-off, they suffer from computational inefficiency and conflicting gradient dynamics during training. We propose F2Net, a frequency-aware framework that decomposes UHR images into high- and low-frequency components for specialized processing. The high-frequency branch preserves full-resolution structural details, while the low-frequency branch processes downsampled inputs through dual sub-branches capturing short- and long-range dependencies. A Hybrid-Frequency Fusion mod- ule integrates these observations, guided by two novel objectives: Cross-Frequency Alignment Loss ensures semantic consistency between frequency components, and Cross-Frequency Balance Loss regulates gradient magnitudes across branches to stabilize training. Evaluated on DeepGlobe and Inria Aerial benchmarks, F2Net achieves state-of-the-art performance with mIoU of 80.22 and 83.39, respectively.

Yifei Zeng, Yajie Bao, Jiachen Qian, Shuang Wu, Youtian Lin, Hao Zhu, Buyu Li, Feihu Zhang, Xun Cao, Yao Yao

Prevailing 3D texture generation methods, which often rely on multi-view fusion, are frequently hindered by inter-view inconsistencies and incomplete coverage of complex surfaces, limiting the fidelity and completeness of the generated content. To overcome these challenges, we introduce TEXTRIX, a native 3D attribute generation framework for high-fidelity texture synthesis and downstream applications such as precise 3D part segmentation. Our approach constructs a latent 3D attribute grid and leverages a Diffusion Transformer equipped with sparse attention, enabling direct coloring of 3D models in volumetric space and fundamentally avoiding the limitations of multi-view fusion. Built upon this native representation, the framework naturally extends to high-precision 3D segmentation by training the same architecture to predict semantic attributes on the grid. Extensive experiments demonstrate state-of-the-art performance on both tasks, producing seamless, high-fidelity textures and accurate 3D part segmentation with precise boundaries.