DM
D. Maksymchuk
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Close physical interactions such as hugs, handshakes, and collaborative tasks are commonplace in everyday life, yet 3D reconstruction methods still struggle to capture them accurately. Most approaches treat each person independently, producing reconstructions where bodies unrealistically intersect, drift apart, or collapse to averaged poses, particularly under occlusion. Furthermore, without an explicit probability model over interactions, there is no principled way to assess whether a reconstruction is plausible in the first place.
We address this by learning an explicit probabilistic prior over close human-human interactions. Using a conditional normalizing flow, we model how one person's pose and position relate to the other's, yielding a tractable likelihood that can be evaluated for any pair of poses at low computational cost, unlike diffusion models, which require iterative sampling chains, or VAEs, whose likelihoods are lower bounds rather than exact densities. Our model serves three purposes: it generates diverse and realistic reaction poses given an observed person, scores the plausibility of arbitrary interaction pairs, and acts as a differentiable regularizer in image-to-mesh optimization to improve two-person 3D reconstruction from a single RGB image. Experiments demonstrate strong performance in generating diverse reaction poses and in discriminating plausible from implausible interaction pairs. Used as an optimization prior, our method improves two-person image-to-mesh reconstruction, with the largest gains in the relative placement of the two bodies. ...
We address this by learning an explicit probabilistic prior over close human-human interactions. Using a conditional normalizing flow, we model how one person's pose and position relate to the other's, yielding a tractable likelihood that can be evaluated for any pair of poses at low computational cost, unlike diffusion models, which require iterative sampling chains, or VAEs, whose likelihoods are lower bounds rather than exact densities. Our model serves three purposes: it generates diverse and realistic reaction poses given an observed person, scores the plausibility of arbitrary interaction pairs, and acts as a differentiable regularizer in image-to-mesh optimization to improve two-person 3D reconstruction from a single RGB image. Experiments demonstrate strong performance in generating diverse reaction poses and in discriminating plausible from implausible interaction pairs. Used as an optimization prior, our method improves two-person image-to-mesh reconstruction, with the largest gains in the relative placement of the two bodies. ...
Close physical interactions such as hugs, handshakes, and collaborative tasks are commonplace in everyday life, yet 3D reconstruction methods still struggle to capture them accurately. Most approaches treat each person independently, producing reconstructions where bodies unrealistically intersect, drift apart, or collapse to averaged poses, particularly under occlusion. Furthermore, without an explicit probability model over interactions, there is no principled way to assess whether a reconstruction is plausible in the first place.
We address this by learning an explicit probabilistic prior over close human-human interactions. Using a conditional normalizing flow, we model how one person's pose and position relate to the other's, yielding a tractable likelihood that can be evaluated for any pair of poses at low computational cost, unlike diffusion models, which require iterative sampling chains, or VAEs, whose likelihoods are lower bounds rather than exact densities. Our model serves three purposes: it generates diverse and realistic reaction poses given an observed person, scores the plausibility of arbitrary interaction pairs, and acts as a differentiable regularizer in image-to-mesh optimization to improve two-person 3D reconstruction from a single RGB image. Experiments demonstrate strong performance in generating diverse reaction poses and in discriminating plausible from implausible interaction pairs. Used as an optimization prior, our method improves two-person image-to-mesh reconstruction, with the largest gains in the relative placement of the two bodies.
We address this by learning an explicit probabilistic prior over close human-human interactions. Using a conditional normalizing flow, we model how one person's pose and position relate to the other's, yielding a tractable likelihood that can be evaluated for any pair of poses at low computational cost, unlike diffusion models, which require iterative sampling chains, or VAEs, whose likelihoods are lower bounds rather than exact densities. Our model serves three purposes: it generates diverse and realistic reaction poses given an observed person, scores the plausibility of arbitrary interaction pairs, and acts as a differentiable regularizer in image-to-mesh optimization to improve two-person 3D reconstruction from a single RGB image. Experiments demonstrate strong performance in generating diverse reaction poses and in discriminating plausible from implausible interaction pairs. Used as an optimization prior, our method improves two-person image-to-mesh reconstruction, with the largest gains in the relative placement of the two bodies.
This paper introduces the Quadrilateral filter, an advanced extension of the Bilateral and Trilateral filters aimed at addressing limitations in high-gradient regions of images. While the Bilateral filter effectively preserves edges during smoothing, it struggles with intensity variations, leading to blunted image details. The trilateral filter improves upon this by incorporating local plane geometry approximations but assumes linear pixel intensity distributions, limiting its effectiveness. The proposed Quadrilateral filter utilizes curvature-based geometry approximations to enhance noise reduction, contrast preservation, artifact reduction, and image reconstruction by accounting for nonlinear pixel value distributions. The development of this filter represents the main contribution of the paper while exploring whether the established Bilateral and Trilateral filters’ performance can be further improved through curvature-based local geometry approximations. The findings demonstrate improvements in image quality and detail preservation, with broad implications for applications in image de-noising, tone-mapping, multimedia processing, and beyond.
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This paper introduces the Quadrilateral filter, an advanced extension of the Bilateral and Trilateral filters aimed at addressing limitations in high-gradient regions of images. While the Bilateral filter effectively preserves edges during smoothing, it struggles with intensity variations, leading to blunted image details. The trilateral filter improves upon this by incorporating local plane geometry approximations but assumes linear pixel intensity distributions, limiting its effectiveness. The proposed Quadrilateral filter utilizes curvature-based geometry approximations to enhance noise reduction, contrast preservation, artifact reduction, and image reconstruction by accounting for nonlinear pixel value distributions. The development of this filter represents the main contribution of the paper while exploring whether the established Bilateral and Trilateral filters’ performance can be further improved through curvature-based local geometry approximations. The findings demonstrate improvements in image quality and detail preservation, with broad implications for applications in image de-noising, tone-mapping, multimedia processing, and beyond.