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The success of 3DGS in generative and editing applications has sparked growing interest in 3DGS-based style transfer. However, current methods still face two major challenges: (1) multi-view inconsistency often leads to style conflicts, resulting in appearance smoothing and distortion; and (2) heavy reliance on VGG features, which struggle to disentangle style and content from style images, often causing content leakage and excessive stylization. To tackle these issues, we introduce FantasyStyle, a 3DGS-based style transfer framework, and the first to rely entirely on diffusion model distillation. It comprises two key components: (1) Multi-View Frequency Consistency. We enhance cross-view consistency by applying a 3D filter to multi-view noisy latent, selectively reducing low-frequency components to mitigate stylized prior conflicts. (2) Controllable Stylized Distillation. To suppress content leakage from style images, we introduce negative guidance to exclude undesired content. In addition, we identify the limitations of Score Distillation Sampling and Delta Denoising Score in 3D style transfer and remove the reconstruction term accordingly. Building on these insights, we propose a controllable stylized distillation that leverages negative guidance to more effectively optimize the 3D Gaussians. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches, achieving higher stylization quality and visual realism across various scenes and styles.

AAAI 2026

FantasyStyle: Controllable Stylized Distillation for 3D Gaussian Splatting

diffusion models for vision

learning & optimization for cv

3d computer vision

poster

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs.
Previous works on training LLMs to leverage external retrievers for solving complex problems have predominantly employed end-to-end reinforcement learning. However, these approaches neglect supervision over the reasoning process, making it difficult to guarantee logical coherence and rigor.
To address these limitations, we propose Thinker, a hierarchical thinking model for deep search through multi-turn interaction, making the reasoning process supervisable and verifiable.
It decomposes complex problems into independently solvable sub-problems, each dually represented in both natural language and an equivalent logical function to support knowledge base and web searches. 
Concurrently, dependencies between sub-problems are passed as parameters via these logical functions, enhancing the logical coherence of the problem-solving process. 
To avoid unnecessary external searches, we perform knowledge boundary determination to check if a sub-problem is within the LLM's intrinsic knowledge, allowing it to answer directly.
Experimental results indicate that with as few as several hundred training samples, the performance of Thinker is competitive with established baselines. 
Furthermore, when scaled to the full training set, Thinker significantly outperforms these methods across various datasets and model sizes.

Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction

3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner. During training, it relies heavily on the average magnitude of view-space positional gradients to grow Gaussians to reduce rendering loss. However, this average operation smooths the positional gradients from different viewpoints and rendering errors from different pixels, hindering the growth and optimization of many defective Gaussians. This leads to strong spurious artifacts in some areas. To address this problem, we propose Hard Gaussian Splatting, dubbed HGS, which considers multi-view significant positional gradients and rendering errors to grow hard Gaussians that fill the gaps of classical Gaussian Splatting on 3D scenes, thus achieving superior NVS results. In detail, we present positional gradient driven HGS, which leverages multi-view significant positional gradients to uncover hard Gaussians. Moreover, we propose rendering error guided HGS, which identifies noticeable pixel rendering errors and potentially over-large Gaussians to jointly mine hard Gaussians. By growing and optimizing these hard Gaussians, our method helps to resolve blurring and needle-like artifacts. Experiments on various datasets demonstrate that our method achieves state-of-the-art rendering quality while maintaining real-time efficiency, yielding LPIPS improvements of **5.1\%**, **19.7\%** and **6.3\%** on Mip-NeRF360, Tanks\&Temples and Deep Blending, respectively..

Pushing Rendering Boundaries: Hard Gaussian Splatting

Vision-Language-Action (VLA) models enable robotic systems to perform embodied tasks but face deployment challenges due to the high computational demands of the dense Large Language Models (LLMs), with existing early-exit-based sparsification methods often overlooking the critical semantic role of final layers in downstream tasks. Aligning with the recent breakthrough of the Shallow Brain Hypothesis (SBH) in neuroscience and the mixture of experts in model sparsification, we conceptualize each LLM layer as an expert and propose a Mixture-of-LayEr Vision Language Action model (MoLe-VLA or simply MoLe) architecture for dynamic LLM layer activation. Specifically, we introduce a Spatial-Temporal Aware Router (STAR) for MoLe to selectively activate only parts of the layers based on the robot’s current state, mimicking the brain's distinct signal pathways specialized for cognition and causal reasoning. Additionally, to compensate for the cognition ability of LLM lost during the layer-skipping, we devise a Cognitive self-Knowledge Distillation (CogKD) to enhance the understanding of task demands and generate task-relevant action sequences by leveraging cognition features. Extensive experiments in RLBench simulations and real-world environments demonstrate the superiority of MoLe-VLA in both efficiency and performance, improving the mean success rate by 9.7\% across ten simulation tasks while accelerating inference by 36.8\% over OpenVLA.

MoLe-VLA: Dynamic Layer-skipping Vision Language Action Model via Mixture-of-Layers for Efficient Robot Manipulation

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities, yet their ability to ground language in complex, interactive environments such as video games remains a critical frontier. Existing benchmarks are inadequate for this purpose: real-world datasets like RefCOCO introduce a domain gap; GUI-centric benchmarks lack the complexity of modern game interfaces; and existing game-specific benchmarks are often too simplistic or narrow, failing to assess fine-grained, generalizable grounding capabilities.
To address this issue, we propose GGBench — a large-scale, cross-genre benchmark designed to probe the grounding capabilities of LVLMs in diverse gaming scenarios. GGBench features unprecedented genre diversity, encompassing 10 categories including card games, first-person shooters, and role-playing games, with a total of 1335 test images. It focuses on tasks that require connecting natural language instructions to specific in-game objects and UI elements.
Experimental results show existing models perform poorly on GGBench, with weak grounding abilities, especially in complex game scenarios. Due to limited data scale, fine-tuning them for gaming scenarios is also challenging. To address this, we propose Game-R1, a novel training method centered on the Grounded Reinforcement Policy Optimization (GRPO) algorithm. GRPO maximizes limited interaction data utility and enables robust few-shot generalization across games. Extensive experiments show Game-R1 significantly outperforms existing LVLMs on GGBench, validating our approach.
GGBench provides a solid and comprehensive evaluation platform for subsequent research on agents in gaming environments, which strongly promotes development in this field.

Game Ground Bench: Probing the Limits of LVLMs in Complex Semantic Grounding Across Game Universes

We propose PFAvatar (Pose-Fusion Avatar), a new method that reconstructs high-quality 3D avatars from ``Outfit of the Day'' (OOTD) photos, which exhibit diverse poses, occlusions, and complex backgrounds. Our method consists of two stages: (1) fine-tuning a pose-aware diffusion model from few-shot OOTD examples and (2) distilling a 3D avatar represented by a neural radiance field (NeRF). In the first stage, unlike previous methods that segment images into assets (e.g. garments, accessories) for 3D assembly, which is prone to inconsistency, we avoid decomposition and directly model the full-body appearance. By integrating a pre-trained ControlNet for pose estimation and a novel Condition Prior Preservation Loss (CPPL), our method enables end-to-end learning of fine details while mitigating language drift in few-shot training. Our method completes personalization in just 5 minutes, achieving a 48$\times$ speed-up compared to previous approaches. In the second stage, we introduce a NeRF-based avatar representation optimized by canonical SMPL-X space sampling and Multi-Resolution 3D-SDS. Compared to mesh-based representations that suffer from resolution-dependent discretization and erroneous occluded geometry, our continuous radiance field can preserve high-frequency textures (e.g., hair) and handle occlusions correctly through transmittance. 
Experiments demonstrate that PFAvatar outperforms state-of-the-art methods in terms of reconstruction fidelity, detail preservation, and robustness to occlusions/truncations, advancing practical 3D avatar generation from real-world OOTD albums. In addition, the reconstructed 3D avatars support downstream applications such as virtual try-on, animation, and human video reenactment, further demonstrating the versatility and practical value of our approach.

PFAvatar: Pose-Fusion 3D Personalized Avatar Reconstruction from Real-World Outfit-of-the-Day Photos

Model merging combines expert models for multitask performance but faces challenges from parameter interference. This has sparked recent interest in controllable model merging, giving users the ability to explicitly balance performance trade-offs. Existing approaches employ a compile-then-query paradigm, performing a costly offline multi-objective optimization to enable fast, preference-aware model generation. This offline stage typically involves iterative search or dedicated training, with complexity that grows exponentially with the number of tasks. To overcome these limitations, we shift the perspective from parameter-space optimization to a direct correction of the model's final representation. Our approach models this correction as an optimal linear transformation, yielding a closed-form solution that replaces the entire offline optimization process with a single-step, architecture-agnostic computation. This solution directly incorporates user preferences, allowing a Pareto-optimal model to be generated on-the-fly with complexity that scales linearly with the number of tasks. Experimental results show our method generates a superior Pareto front with more precise preference alignment and drastically reduced computational cost. Code is available at: https://github.com/CREAHDD/ReACT

From Parameter to Representation: A Closed-Form Approach for Controllable Model Merging

3D Gaussian Splatting (3DGS) has recently demonstrated significant potential for streaming dynamic scenes, enabling the synthesis of photo-realistic and real-time free-viewpoint videos (FVVs). Conventional streaming pipelines optimize each frame independently, \textit{i.e.}, the attribute of the 3D Gaussians (3DGs) responsible for the static regions are supposed to be identical across all frames but are changed in the optimization process, thus causing temporal color inconsistency and visual flickering artifacts in the static regions. To tackle this, we propose CPOStream, which utilizes a prediction and observation module to determine the state of 3DG. Specifically, the prediction module records those 3DGs that are inactive in the past K frames and those would be ignored in the optimization process of the current frame reconstruction. Thus, the attributes of those 3DGs would be kept consistent across the past K frames, guaranteeing the temporal consistence. Additionally, the observation module conducts motion detection, and recognizes those new 3DGs which are not recorded in the prediction module and are first detected by the observation module in the past K frames. The attributes of those 3DGs are optimized during the current frame reconstruction. Experiments on multiple real-world FVV benchmarks show that CPOStream substantially reduces temporal flickering and improves reconstruction fidelity, achieving state‑of‑the‑art performance.

CPOStream: Collaborating Prediction and Observation for Flicker-Free Streamable Free-Viewpoint Video with 3DGS

Attributing synthetic images to their source generative models is critical for digital forensics and security. While most existing attribution methods can distinguish images produced by known models and reject those from unknown ones, they are unable to verify whether a given image was produced by a specific, previously unseen model. To address this limitation, we formulate an open-set verification problem: determining whether a given image was generated by a specific model. Our key insight is that synthetic images from different models show consistent, content-independent fingerprints in their amplitude spectrum. Based on this insight, we design a dynamic fingerprint simulator capable of simulating over 1.6 trillion generative model architectures. We further train an extractor to capture model-specific fingerprint representations with supervised contrastive learning, enabling accurate attribution of synthetic images, even from previously unseen models. Our method does not rely on any synthetic images, instead, it is trained solely on real images. On DMDetection and AIGCBenchmark, which comprises dozens of state-of-the-art and in-the-wild generative models, our method improves the attribution performance (AUC) of the prior method from random level to 94.05\% and 83.05\%, respectively. On GenImage and OSMA datasets, we obtain 85.08\%, and 88.48\% OSCR, outperforming the SOTA methods by 4.30\% and 9.37\% under the same settings.

One for All: Synthesis-Free Fingerprint Learning for Attribution of In-the-Wild Synthetic Images

In-context learning (ICL) offers a promising paradigm for universal medical image analysis, enabling models to perform diverse image processing tasks without retraining. However, current ICL models for medical imaging remain limited in two critical aspects: they cannot simultaneously achieve high-fidelity predictions and global anatomical understanding, and there is no unified model trained across diverse medical imaging tasks (e.g., segmentation and enhancement) and anatomical regions. As a result, the full potential of ICL in medical imaging remains underexplored. Thus, we present Medverse, a universal ICL model for 3D medical imaging, trained on 22 datasets covering diverse tasks in universal image segmentation, transformation, and enhancement across multiple organs, imaging modalities, and clinical centers. Medverse employs a next-scale autoregressive in-context learning framework that progressively refines predictions from coarse to fine, generating consistent, full-resolution volumetric outputs and enabling multi-scale anatomical awareness. We further propose a blockwise cross-attention module that facilitates long-range interactions between context and target inputs while preserving computational efficiency through spatial sparsity. Medverse is extensively evaluated on a broad collection of held-out datasets covering previously unseen clinical centers, organs, species, and imaging modalities. Results demonstrate that Medverse substantially outperforms existing ICL baselines and establishes a novel paradigm for in-context learning. Code and model weights will be made publicly available.

Medverse: A Universal Model for Full-Resolution 3D Medical Image Segmentation, Transformation and Enhancement

Egocentric human pose estimation (HPE) plays a crucial role in immersive applications such as virtual and augmented reality. However, existing methods relying on either visual or sparse inertial data alone often suffer from occlusion or ill-posed problems. In this work, we propose SAME, a novel spatial-aware multimodal fusion framework combining the complementary signals from the stereo images and sparse IMUs for accurate and robust egocentric HPE. It adopts a two-stage network based on a dual coordinate frame to mitigate the coordinate inconsistencies among the stereo cameras and the IMUs. In the first stage, the IMU signals are transformed into the local frame and iteratively fused with the stereo images for estimating 3D poses in the local frame. In the second stage, the local poses are transformed into the global frame with the 6DOF head poses provided by the head-mounted display's (HMD) SLAM algorithm and then temporally aggregated via a temporal Transformer network. Meanwhile, to achieve geometric and semantic alignment among multi-modal features, we present a depth-guided spatial-aware deformable stereo attention network and a modality-aware Transformer decoder for cross-view and cross-modal feature fusion. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on the public EMHI multi-modal egocentric pose estimation benchmark.

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Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction

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