The rapid evolution of deepfake technologies demands robust and reliable face forgery detection algorithms. While determining whether an image has been manipulated remains essential, the ability to precisely localize forgery clues is also important for enhancing model explainability and building user trust. To address this dual challenge, we introduce DiffusionFF, a diffusion-based framework that simultaneously performs face forgery detection and fine-grained artifact localization. Our key idea is to establish a novel encoder-decoder architecture: a pretrained forgery detector serves as a powerful "artifact encoder", and a denoising diffusion model is repurposed as an "artifact decoder". Conditioned on multi-scale forgery-related features extracted by the encoder, the decoder progressively synthesizes a detailed artifact localization map. We then fuse this fine-grained localization map with high-level semantic features from the forgery detector, leading to substantial improvements in detection capability. Extensive experiments show that DiffusionFF achieves state-of-the-art (SOTA) performance across multiple benchmarks, underscoring its superior effectiveness and explainability.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Motion capture now underpins content creation far beyond digital humans, yet most pipelines remain species- or template-specific. We formalize this gap as Category-Agnostic Motion Capture (CAMoCap): given a monocular video and an arbitrary rigged 3D asset as a prompt, the goal is to reconstruct a rotation-based animation (e.g., BVH) that directly drives the specific asset. We present MoCapAnything, a reference-guided, factorized framework that first predicts 3D joint trajectories and then recovers asset-specific rotations via constraint-aware Inverse Kinematics (IK) Fitting. MoCapAnything comprises three learnable modules and a lightweight IK stage: a Reference Prompt Encoder that distills per-joint queries from the asset's skeleton, mesh, and rendered image set; a Video Feature Extractor that computes dense visual descriptors and reconstructs a coarse 4D deforming mesh to bridge the modality gap between RGB tokens and the point-cloud-like joint space; and a Unified Motion Decoder that fuses these cues to produce temporally coherent trajectories. We also curate Truebones Zoo with 1,038 motion clips, each providing a standardized skeleton-mesh-rendered-video triad. Experiments on in-domain benchmarks and in-the-wild videos show that MoCapAnything delivers high-quality skeletal animations and exhibits non-trivial cross-species retargeting across heterogeneous rigs, offering a scalable path toward prompt-based 3D motion capture for arbitrary assets. The codes are available on our project page: https://animotionlab.github.io/MoCapAnything/
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Open-vocabulary 3D object affordance grounding aims to identify functional regions of objects given arbitrary semantic descriptions. However, existing methods often rely on fixed training categories and geometric priors, lacking geometric invariance and analogical reasoning capabilities. Since there exists a significant domain gap when transferring affordance knowledge learned from 2D images to 3D point clouds, existing methods struggle to generalize well to objects with diverse shapes or unseen categories, and fail to perform effective category reasoning.To address these challenges, we propose **QueryMe**, a **Query**-driven framework that learns from **M**ultimodal **e**vidence spaces to achieve open-vocabulary 3D affordance grounding.The proposed approach is to project human-object interaction images into 3D space, employ an Adaptive Spatial Attention module to focus on key interaction regions, and introduce a multimodal query structure to retrieve available geometrically consistent functional parts within the point cloud, effectively fusing visual, linguistic, and geometric cues.Leveraging attention-based query mechanisms, our method adaptively localizes affordance regions and performs analogy reasoning through geometric similarity, thereby exhibiting strong generalization to unseen scenes and objects. Experimental results demonstrate that QueryMe consistently outperforms state-of-the-art approaches, with the AUC improving by 4.19% compared to previous work for unseen affordance grounding tasks.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表