Recovering 3D object pose and shape from a single image is a challenging and 
                                    ill-posed problem. This is due to strong (self-)occlusions, depth ambiguities, 
                                    the vast intra- and inter-class shape variance, and the lack of 3D ground truth 
                                    for natural images. Existing deep-network methods are trained on synthetic datasets 
                                    to predict 3D shapes, so they often struggle generalizing to real-world images. 
                                    Moreover, they lack an explicit feedback loop for refining noisy estimates, and 
                                    primarily focus on geometry without directly considering pixel alignment. To tackle 
                                    these limitations, we develop a novel render-and-compare optimization framework, 
                                    called SDFit. This has three key innovations: First, it uses a learned category-specific 
                                    and morphable signed-distance-function (mSDF) model, and fits this to an image by 
                                    iteratively refining both 3D pose and shape. The mSDF robustifies inference by 
                                    constraining the search on the manifold of valid shapes, while allowing for arbitrary 
                                    shape topologies. Second, SDFit retrieves an initial 3D shape that likely matches 
                                    the image, by exploiting foundational models for efficient look-up into 3D shape 
                                    databases. Third, SDFit initializes pose by establishing rich 2D-3D correspondences 
                                    between the image and the mSDF through foundational features. We evaluate SDFit on 
                                    three image datasets, i.e., Pix3D, Pascal3D+, and COMIC. SDFit performs on par with 
                                    SotA feed-forward networks for unoccluded images and common poses, but is uniquely 
                                    robust to occlusions and uncommon poses. Moreover, it requires no retraining for 
                                    unseen images. Thus, SDFit contributes new insights for generalizing in the wild.
                                    

                                        @inproceedings{antic2025sdfit,
                                          title     = {{SDFit}: {3D} Object Pose and Shape by Fitting a Morphable {SDF} to a Single Image},
                                          author    = {Anti\'{c}, Dimitrije and Paschalidis, Georgios and Tripathi, Shashank and Gevers, Theo and Dwivedi, Sai Kumar and Tzionas, Dimitrios},
                                          booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
                                          month     = {October},
                                          year      = {2025},
                                        }
                                    

                                        Synthesizing 3D whole bodies that realistically grasp objects is useful for
                                        animation, mixed reality, and robotics. This is challenging, because the hands
                                        and body need to look natural w.r.t. each other, the grasped object, as well as
                                        the local scene (i.e., a receptacle supporting the object). Moreover, training
                                        data for this task is really scarce, while capturing new data is expensive.
                                        Recent work goes beyond finite datasets via a divide-and-conquer approach; it
                                        first generates a “guiding” right-hand grasp, and then searches for bodies that
                                        match this. However, the guiding-hand synthesis lacks controllability and
                                        receptacle awareness, so it likely has an implausible direction (i.e., a body
                                        can’t match this without penetrating the receptacle) and needs corrections
                                        through major post-processing. Moreover, the body search needs exhaustive
                                        sampling and is expensive. These are strong limitations. We tackle these with a
                                        novel method called CWGrasp. Our key idea is that performing geometry-based
                                        reasoning “early on,” instead of “too late,” provides rich “control” signals
                                        for inference. To this end, CWGrasp first samples a plausible
                                        reaching-direction vector (used later for both the arm and hand) from a
                                        probabilistic model built via ray-casting from the object and collision
                                        checking. Then, it generates a reaching body with a desired arm direction, as
                                        well as a “guiding” grasping hand with a desired palm direction that complies
                                        with the arm’s one. Eventually, CWGrasp refines the body to match the “guiding”
                                        hand, while plausibly contacting the scene. Notably, generating
                                        already-compatible “parts” greatly simplifies the “whole”. Moreover, CWGrasp
                                        uniquely tackles both right and left-hand grasps. We evaluate on the GRAB and
                                        ReplicaGrasp datasets. CWGrasp outperforms baselines, at lower runtime and
                                        budget, while all components help performance. Code and models are available for
                                        for research.
                                    

                                        @inproceedings{paschalidis2025cwgrasp,
                                        title={{3D} {W}hole-Body Grasp Synthesis with Directional Controllability},
                                        author={Paschalidis, Georgios and Wilschut, Romana and Anti\'{c}, Dimitrije and Taheri, Omid and Tzionas, Dimitrios},
                                        booktitle = {{International Conference on 3D Vision (3DV)}},
                                        year={2025}
                                        }