GenAI4ELab/papercli-papers huggingface dataset:
Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding
Authors: Wenxuan Guo; Xiuwei Xu; Ziwei Wang; Jianjiang Feng; Jie Zhou; Jiwen Lu
Venue: CVPR 2025 (main)

Abstract: In this paper, we propose an efficient multi-level convolution architecture for 3D visual grounding. Conventional methods are difficult to meet the requirements of real-time inference due to the two-stage or point-based architecture. Inspired by the success of multi-level fully sparse convolutional architecture in 3D object detection, we aim to build a new 3D visual grounding framework following this technical route. However, as in 3D visual grounding task the 3D scene representation should be deeply interacted with text features, sparse convolution-based architecture is inefficient for this interaction due to the large amount of voxel features. To this end, we propose text-guided pruning (TGP) and completion-based addition (CBA) to deeply fuse 3D scene representation and text features in an efficient way by gradual region pruning and target completion. Specifically, TGP iteratively sparsifies the 3D scene representation and thus efficiently interacts the voxel features with text features by cross-attention. To mitigate the affect of pruning on delicate geometric information, CBA adaptively fixes the over-pruned region by voxel completion with negligible computational overhead. Compared with previous single-stage methods, our method achieves top inference speed and surpasses previous fastest method by 100% FPS. Our method also achieves state-of-the-art accuracy even compared with two-stage methods, with +1.13 lead of Acc@0.5 on ScanRefer, and +2.6 and +3.2 leads on NR3D and SR3D respectively. The code is available at https://github.com/GWxuan/TSP3D.
ROCKET-1: Mastering Open-World Interaction with Visual-Temporal Context Prompting
Authors: Shaofei Cai; Zihao Wang; Kewei Lian; Zhancun Mu; Xiaojian Ma; Anji Liu; Yitao Liang
Venue: CVPR 2025 (main)

Abstract: Vision-language models (VLMs) have excelled in multimodal tasks, but adapting them to embodied decision-making in open-world environments presents challenges. One critical issue is bridging the gap between discrete entities in low-level observations and the abstract concepts required for effective planning. A common solution is building hierarchical agents, where VLMs serve as high-level reasoners that break down tasks into executable sub-tasks, typically specified using language. However, language suffers from the inability to communicate detailed spatial information. We propose visual-temporal context prompting, a novel communication protocol between VLMs and policy models. This protocol leverages object segmentation from past observations to guide policy-environment interactions. Using this approach, we train ROCKET-2, a low-level policy that predicts actions based on concatenated visual observations and segmentation masks, supported by real-time object tracking from SAM2. Our method unlocks the potential of VLMs, enabling them to tackle complex tasks that demand spatial reasoning.  Experiments in Minecraft show that our approach enables agents to achieve previously unattainable tasks, with a 76% absolute improvement in open-world interaction performance. Codes are available at https://craftjarvis.github.io/ROCKET-1.
Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space
Authors: Leonhard Sommer; Olaf Dünkel; Christian Theobalt; Adam Kortylewski
Venue: CVPR 2025 (main)

Abstract: 3D morphable models (3DMMs) are a powerful tool to represent the possible shapes and appearances of an object category. Given a single test image, 3DMMs can be used to solve various tasks, such as predicting the 3D shape, pose, semantic correspondence, and instance segmentation of an object. Unfortunately, 3DMMs are only available for very few object categories that are of particular interest, like faces or human bodies, as they require a demanding 3D data acquisition and category-specific training process. In contrast, we introduce a new method, Common3D, that learns 3DMMs of common objects in a fully self-supervised manner from a collection of object-centric videos. For this purpose, our model represents objects as a learned 3D template mesh and a deformation field that is parameterized as an image-conditioned neural network. Different from prior works, Common3D represents the object appearance with neural features instead of RGB colors, which enables the learning of more generalizable representations through an abstraction from pixel intensities. Importantly, we train the appearance features using a contrastive objective by exploiting the correspondences defined through the deformable template mesh. This leads to higher quality correspondence features compared to related works and a significantly improved model performance at estimating 3D object pose and semantic correspondence. Common3D is the first completely self-supervised method that can solve various vision tasks in a zero-shot manner. github.com/GenIntel/common3d.
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
Authors: Tianyu Yu; Haoye Zhang; Qiming Li; Qixin Xu; Yuan Yao; Da Chen; Xiaoman Lu; Ganqu Cui; Yunkai Dang; Taiwen He; Xiaocheng Feng; Jun Song; Bo Zheng; Zhiyuan Liu; Tat-Seng Chua; Maosong Sun
Venue: CVPR 2025 (main)

Abstract: Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models.This leaves the community without  foundational knowledge about how to build high-quality feedback with open-source MLLMs.In this work, we introduce RLAIF-V, a novel framework that aligns MLLMs in a fully open-source paradigm. RLAIF-V maximally explores open-source MLLMs from two perspectives, including high-quality feedback data generation for preference learning and self-feedback guidance for inference-time scaling.Extensive experiments on seven benchmarks in both automatic and human evaluation show that RLAIF-V substantially enhances the trustworthiness of models at both preference learning and inference time. RLAIF-V 7B reduces object hallucination by 80.7% and overall hallucination by 33.7%. Remarkably, RLAIF-V 12B further reveals the self-alignment potential of open-source MLLMs, where the  model can learn from feedback of itself to achieve super GPT-4V trustworthiness.
ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression
Authors: Wei Jiang; Junru Li; Kai Zhang; Li Zhang
Venue: CVPR 2025 (main)

Abstract: In Learned Video Compression (LVC), improving inter prediction, such as enhancing temporal context mining and mitigating accumulated errors, is crucial for boosting rate-distortion performance. Existing LVCs mainly focus on mining the temporal movements while neglecting non-local correlations among frames. Additionally, current contextual video compression models use a single reference frame, which is insufficient for handling complex movements. To address these issues, we propose leveraging non-local correlations across multiple frames to enhance temporal priors, significantly boosting rate-distortion performance. To mitigate error accumulation, we introduce a partial cascaded fine-tuning strategy that supports fine-tuning on full-length sequences with constrained computational resources. This method reduces the train-test mismatch in sequence lengths and significantly decreases accumulated errors. Based on the proposed techniques, we present a video compression scheme ECVC. Experiments demonstrate that our ECVC achieves state-of-the-art performance, reducing 10.5% and 11.5% more bit-rates than previous SOTA method DCVC-FM over VTM-13.2 low delay B (LDB) under the intra period (IP) of 32 and -1, respectively. Our code is available at https://github.com/JiangWeibeta/ECVC.
LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity
Authors: Hongjie Wang; Chih-Yao Ma; Yen-Cheng Liu; Ji Hou; Tao Xu; Jialiang Wang; Felix Juefei-Xu; Yaqiao Luo; Peizhao Zhang; Tingbo Hou; Peter Vajda; Niraj K. Jha; Xiaoliang Dai
Venue: CVPR 2025 (main)

Abstract: Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the number of pixels.  This makes minute-length video generation extremely expensive, limiting most existing models to generating videos of only 10-20 seconds length. We propose a Linear-complexity text-to-video Generation (LinGen) framework whose cost scales linearly in the number of pixels. For the first time, LinGen enables high-resolution minute-length video generation on a single GPU without compromising quality. It replaces the computationally-dominant and quadratic-complexity block, self-attention, with a linear-complexity block called MATE, which consists of an MA-branch and a TE-branch. The MA-branch targets short-to-long-range correlations, combining a bidirectional Mamba2 block with our token rearrangement method, Rotary Major Scan, and our review tokens developed for long video generation. The TE-branch is a novel TEmporal Swin Attention block that focuses on temporal correlations between adjacent tokens and medium-range tokens. The MATE block addresses the adjacency preservation issue of Mamba and improves the consistency of generated videos significantly. Experimental results show that  LinGen outperforms DiT (with a 75.6% win rate) in video quality with up to 15x(11.5x) FLOPs (latency) reduction.  Furthermore, both automatic metrics and human evaluation demonstrate our LinGen-4B yields comparable video quality to state-of-the-art models (with a 50.5%, 52.1%, 49.1% win rate with respect to Gen-3, LumaLabs, and Kling, respectively). This paves the way to hour-length movie generation and real-time interactive video generation. Project website: https://lineargen.github.io/.
EditSplat: Multi-View Fusion and Attention-Guided Optimization for View-Consistent 3D Scene Editing with 3D Gaussian Splatting
Authors: Dong In Lee; Hyeongcheol Park; Jiyoung Seo; Eunbyung Park; Hyunje Park; Ha Dam Baek; Sangheon Shin; Sangmin Kim; Sangpil Kim
Venue: CVPR 2025 (main)

Abstract: Recent advancements in 3D editing have highlighted the potential of text-driven methods in real-time, user-friendly AR/VR applications. However, current methods rely on 2D diffusion models without adequately considering multi-view information, resulting in multi-view inconsistency. While 3D Gaussian Splatting (3DGS) significantly improves rendering quality and speed, its 3D editing process encounters difficulties with inefficient optimization, as pre-trained Gaussians retain excessive source information, hindering optimization. To address these limitations, we propose EditSplat, a novel 3D editing framework that integrates Multi-view Fusion Guidance (MFG) and Attention-Guided Trimming (AGT). Our MFG ensures multi-view consistency by incorporating essential multi-view information into the diffusion process, leveraging classifier-free guidance from the text-to-image diffusion model and the geometric properties of 3DGS. Additionally, our AGT leverages the explicit representation of 3DGS to selectively prune and optimize 3D Gaussians, enhancing optimization efficiency and enabling precise, semantically rich local edits. Through extensive qualitative and quantitative evaluations, EditSplat achieves superior multi-view consistency and editing quality over existing methods, significantly enhancing overall efficiency.
SpatialCLIP: Learning 3D-aware Image Representations from Spatially Discriminative Language
Authors: Zehan Wang; Sashuai Zhou; Shaoxuan He; Haifeng Huang; Lihe Yang; Ziang Zhang; Xize Cheng; Shengpeng Ji; Tao Jin; Hengshuang Zhao; Zhou Zhao
Venue: CVPR 2025 (main)

Abstract: Contrastive Language-Image Pre-training (CLIP) learns robust visual models through language supervision, making it a crucial visual encoding technique for various applications. However, CLIP struggles with comprehending spatial concepts in images, potentially restricting the spatial intelligence of CLIP-based AI systems. In this work, we propose SpatialCLIP, an enhanced version of CLIP with better spatial understanding capabilities. To capture the intricate 3D spatial relationships in images, we improve both "visual model" and "language supervision" of CLIP. Specifically, we design 3D-inspired ViT to replace the standard ViT in CLIP. By lifting 2D image tokens into 3D space and incorporating design insights from point cloud networks, our visual model gains greater potential for spatial perception. Meanwhile, captions with accurate and detailed spatial information are very rare. To explore better language supervision for spatial understanding, we re-caption images and perturb their spatial phrases as negative descriptions, which compels the visual model to seek spatial cues to distinguish these hard negative captions. With the enhanced visual model, we introduce SpatialLLaVA, following the same LLaVA-1.5 training protocol, to investigate the importance of visual representations for MLLM's spatial intelligence. Furthermore, we create SpatialBench, a benchmark specifically designed to evaluate CLIP and MLLM in spatial reasoning. SpatialCLIP and SpatialLLaVA achieve substantial performance improvements, demonstrating stronger capabilities in spatial perception and reasoning, while maintaining comparable results on general-purpose benchmarks.
Mono2Stereo: A Benchmark and Empirical Study for Stereo Conversion
Authors: Songsong Yu; Yuxin Chen; Zhongang Qi; Zeke Xie; Yifan Wang; Lijun Wang; Ying Shan; Huchuan Lu
Venue: CVPR 2025 (main)

Abstract: With the rapid proliferation of 3D devices and the shortage of 3D content, stereo conversion is attracting increasing attention. Recent works introduce pretrained Diffusion Models (DMs) into this task. However, due to the scarcity of large-scale training data and comprehensive benchmarks, the optimal methodologies for employing DMs in stereo conversion and the accurate evaluation of stereo effects remain largely unexplored. In this work, we introduce the Mono2Stereo dataset, providing high-quality training data and benchmark to support in-depth exploration of stereo conversion. With this dataset, we conduct an empirical study that yields two primary findings. 1) The differences between the left and right views are subtle, yet existing metrics consider overall pixels, failing to concentrate on regions critical to stereo effects. 2) Mainstream methods adopt either one-stage left-to-right generation or warp-and-inpaint pipeline, facing challenges of degraded stereo effect and image distortion respectively. Based on these findings, we introduce a new evaluation metric, Stereo Intersection-over-Union, which prioritizes disparity and achieves a high correlation with human judgments on stereo effect. Moreover, we propose a strong baseline model, harmonizing the stereo effect and image quality simultaneously, and notably surpassing current mainstream methods. Our code and data will be open-sourced to promote further research in stereo conversion.
Towards Open-Vocabulary Audio-Visual Event Localization
Authors: Jinxing Zhou; Dan Guo; Ruohao Guo; Yuxin Mao; Jingjing Hu; Yiran Zhong; Xiaojun Chang; Meng Wang
Venue: CVPR 2025 (main)

Abstract: The Audio-Visual Event Localization (AVEL) task aims to temporally locate and classify video events that are both audible and visible.Most research in this field assumes a closed-set setting, which restricts these models' ability to handle test data containing event categories absent (unseen) during training. Recently, a few studies have explored AVEL in an open-set setting, enabling the recognition of unseen events as "unknown", but without providing category-specific semantics.In this paper, we advance the field by introducing the Open-Vocabulary Audio-Visual Event Localization (OV-AVEL) problem, which requires localizing audio-visual events and predicting explicit categories for both seen and unseen data at inference.To address this new task, we propose the OV-AVEBench dataset, comprising 24,800 videos across 67 real-life audio-visual scenes (seen:unseen = 46:21), each with manual segment-level annotation.We also establish three evaluation metrics for this task.Moreover, we investigate two baseline approaches, one training-free and one using a further fine-tuning paradigm.Specifically, we utilize the unified multimodal space from the pretrained ImageBind model to extract audio, visual, and textual (event classes) features.The training-free baseline then determines predictions by comparing the consistency of audio-text and visual-text feature similarities.The fine-tuning baseline incorporates lightweight temporal layers to encode temporal relations within the audio and visual modalities, using OV-AVEBench training data for model fine-tuning.We evaluate these baselines on the proposed OV-AVEBench dataset and discuss potential directions for future work in this new field.
One-shot 3D Object Canonicalization based on Geometric and Semantic Consistency
Authors: Li Jin; Yujie Wang; Wenzheng Chen; Qiyu Dai; Qingzhe Gao; Xueying Qin; Baoquan Chen
Venue: CVPR 2025 (main)

Abstract: 3D object canonicalization is a fundamental task, essential for various downstream tasks. Existing methods rely on either cumbersome manual processes or priors learned from extensive, per-category training samples. Real-world datasets, however, often exhibit long-tail distributions, challenging existing learning-based methods, especially in categories with limited samples. We address this by introducing the first one-shot category-level object canonicalization framework that operates under arbitrary poses, requiring only a single canonical model as a reference (the "prior model") for each category. To canonicalize any object, our framework first extracts semantic cues with large language models (LLMs) and vision-language models (VLMs) to establish correspondences with the prior model. We introduce a novel joint energy function to enforce geometric and semantic consistency, aligning object orientations precisely despite significant shape variations. Moreover, we adopt a support-plane strategy to reduce search space for initial poses and utilize a semantic relationship map to select the canonical pose from multiple hypotheses. Extensive experiments on multiple datasets demonstrate that our framework achieves state-of-the-art performance and validates key design choices. Using our framework, we create the Canonical Objaverse Dataset (COD), canonicalizing 32K samples in the Objaverse-LVIS dataset, underscoring the effectiveness of our framework on handling large-scale datasets. Project page at https://jinli998.github.io/One-shot_3D_Object_Canonicalization/
Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning
Authors: Hanxun Yu; Wentong Li; Song Wang; Junbo Chen; Jianke Zhu
Venue: CVPR 2025 (main)

Abstract: Despite encouraging progress in 3D scene understanding, it remains challenging to develop an effective Large Multi-modal Model (LMM) that is capable of understanding and reasoning in complex 3D environments. Most previous methods typically encode 3D point and 2D image features separately, neglecting interactions between 2D semantics and 3D object properties, as well as the spatial relationships within the 3D environment. This limitation not only hinders comprehensive representations of 3D scene, but also compromises training and inference efficiency. To address these challenges, we propose a unified Instance-aware 3D Large Multi-modal Model (Inst3D-LMM) to deal with multiple 3D scene understanding tasks simultaneously. To obtain the fine-grained instance-level visual tokens, we first introduce a novel Multi-view Cross-Modal Fusion (MCMF) module to inject the multi-view 2D semantics into their corresponding 3D geometric features. For scene-level relation-aware tokens, we further present a 3D Instance Spatial Relation (3D-ISR) module to capture the intricate pairwise spatial relationships among objects. Additionally, we perform end-to-end multi-task instruction tuning simultaneously without the subsequent task-specific fine-tuning. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods across 3D scene understanding, reasoning and grounding tasks. Source code is available at: https://github.com/hanxunyu/Inst3D-LMM.
S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting
Authors: Yecong Wan; Mingwen Shao; Yuanshuo Cheng; Wangmeng Zuo
Venue: CVPR 2025 (main)

Abstract: In this paper, we aim ambitiously for a realistic yet challenging problem, namely, how to reconstruct high-quality 3D scenes from sparse low-resolution views that simultaneously suffer from deficient perspectives and contents. Whereas existing methods only deal with either sparse views or low-resolution observations, they fail to handle such hybrid and complicated scenarios. To this end, we propose a novel Sparse-view Super-resolution 3D Gaussian Splatting framework, dubbed S2Gaussian, that can reconstruct structure-accurate and detail-faithful 3D scenes with only sparse and low-resolution views. The S2Gaussian operates in a two-stage fashion. In the first stage, we initially optimize a low-resolution Gaussian representation with depth regularization and densify it to initialize the high-resolution Gaussians through a tailored Gaussian Shuffle operation. In the second stage, we refine the high-resolution Gaussians with the super-resolved images generated from both original sparse views and pseudo-views rendered by the low-resolution Gaussians. In which a customized blur-free inconsistency modeling scheme and a 3D robust optimization strategy are elaborately designed to mitigate multi-view inconsistency and eliminate erroneous updates caused by imperfect supervision. Extensive experiments demonstrate superior results and in particular establishing new state-of-the-art performances with more consistent geometry and finer details. Project Page https://jeasco.github.io/S2Gaussian/.
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution
Authors: Yuxuan Jiang; Ho Man Kwan; Tianhao Peng; Ge Gao; Fan Zhang; Xiaoqing Zhu; Joel Sole; David Bull
Venue: CVPR 2025 (main)

Abstract: Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (ISR). INR-based ISR methods typically learn continuous representations, providing flexibility for generating high-resolution images at any desired scale from their low-resolution counterparts. However, existing INR-based ISR methods utilize multi-layer perceptrons for parameterization in the network; this does not take account of the hierarchical structure existing in local sampling points and hence constrains the representation capability. In this paper, we propose a new Hierarchical encoding based Implicit Image Function for continuous image super-resolution, HIIF, which leverages a novel hierarchical positional encoding that enhances the local implicit representation, enabling it to capture fine details at multiple scales. Our approach also embeds a multi-head linear attention mechanism within the implicit attention network by taking additional non-local information into account. Our experiments show that, when integrated with different backbone encoders, HIIF outperforms the state-of-the-art continuous image super-resolution methods by up to 0.17dB in PSNR. The source code of HIIF will be made publicly available at www.github.com.