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1. Advancing ai-powered medical image synthesis: Insights from medvqa-gi challenge using clip, fine-tuned stable diffusion, and dream-booth + lora

   Ojonugwa, E; Rahman, M; Khalifa, F (September 2024, CEUR workshop proceedings)

   Faggioli, G; Ferro, N; Galuščáková, P; Herrera, A (Ed.)

   The MEDVQA-GI challenge addresses the integration of AI-driven text-to-image generative models in medical diagnostics, aiming to enhance diagnostic capabilities through synthetic image generation. Existing methods primarily focus on static image analysis and lack the dynamic generation of medical imagery from textual descriptions. This study intends to partially close this gap by introducing a novel approach based on fine-tuned generative models to generate dynamic, scalable, and precise images from textual descriptions. Particularly, our system integrates fine-tuned Stable Diffusion and DreamBooth models, as well as Low-Rank Adaptation (LORA), to generate high-fidelity medical images. The problem is around two sub-tasks namely: image synthesis (IS) and optimal prompt production (OPG). The former creates medical images via verbal prompts, whereas the latter provides prompts that produce high-quality images in specified categories. The study emphasizes the limitations of traditional medical image generation methods, such as hand sketching, constrained datasets, static procedures, and generic models. Our evaluation measures showed that Stable Diffusion surpasses CLIP and DreamBooth + LORA in terms of producing high-quality, diversified images. Specifically, Stable Diffusion had the lowest Fréchet Inception Distance (FID) scores (0.099 for single center, 0.064 for multi-center, and 0.067 for combined), indicating higher image quality. Furthermore, it had the highest average Inception Score (2.327 across all datasets), indicating exceptional diversity and quality. This advances the field of AI-powered medical diagnosis. Future research will concentrate on model refining, dataset augmentation, and ethical considerations for efficiently implementing these advances into clinical practice. 

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2. HuMoR: 3D Human Motion Model for Robust Pose EstimationHuMoR: 3D Human Motion Model for Robust Pose Estimation

   Rempe, Davis and (January 2021, International Conference on Computer Vision (ICCV))

   We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose and shape. Though substantial progress has been made in estimating 3D human motion and shape from dynamic observations, recovering plausible pose sequences in the presence of noise and occlusions remains a challenge. For this purpose, we propose an expressive generative model in the form of a conditional variational autoencoder, which learns a distribution of the change in pose at each step of a motion sequence. Furthermore, we introduce a flexible optimization-based approach that leverages HuMoR as a motion prior to robustly estimate plausible pose and shape from ambiguous observations. Through extensive evaluations, we demonstrate that our model generalizes to diverse motions and body shapes after training on a large motion capture dataset, and enables motion reconstruction from multiple input modalities including 3D keypoints and RGB(-D) videos. 

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3. HuMoR: 3D Human Motion Model for Robust Pose Estimation

   https://doi.org/10.1109/ICCV48922.2021.01129  

   Rempe, Davis; Birdal, Tolga; Hertzmann, Aaron; Yang, Jimei; Sridhar, Srinath; Guibas, Leonidas J. (October 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV))

   We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose and shape. Though substantial progress has been made in estimating 3D human motion and shape from dynamic observations, recovering plausible pose sequences in the presence of noise and occlusions remains a challenge. For this purpose, we propose an expressive generative model in the form of a conditional variational autoencoder, which learns a distribution of the change in pose at each step of a motion sequence. Furthermore, we introduce a flexible optimization-based approach that leverages HuMoR as a motion prior to robustly estimate plausible pose and shape from ambiguous observations. Through extensive evaluations, we demonstrate that our model generalizes to diverse motions and body shapes after training on a large motion capture dataset, and enables motion reconstruction from multiple input modalities including 3D keypoints and RGB(-D) videos. See the project page at geometry.stanford.edu/projects/humor. 

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4. Generation of Novel Fall Animation with Configurable Attributes

   https://doi.org/10.1145/3658852.3659087  

   Peng, Siyuan; Ladenheim, Kate; Shrestha, Snehesh; Fermüller, Cornelia (May 2024, ACM)

   none (Ed.)

   It takes less than half a second for a person to fall [8]. Capturing the essence of a fall from video or motion capture is difficult. More generally, generating realistic 3D human body motions from motion capture (MoCap) data is a significant challenge with potential applications in animation, gaming, and robotics. Current motion datasets contain single-labeled activities, which lack fine-grained control over the motion, particularly for actions as sparse, dynamic, and complex as falling. This work introduces a novel human falling dataset and a learned multi-branch, Attribute-Conditioned Variational Autoencoder model to generate novel falls. Our unique dataset introduces a new ontology of the motion into three phases: Impact, Glitch, and Fall. Each branch of the model learns each phase separately and the fusion layer learns to fuse the latent space together. Furthermore, we present data augmentation techniques and an inter-phase smoothness loss for natural plausible motion generation. We successfully generated high-quality images, validating the efficacy of our model in producing high-fidelity, attribute-conditioned human movements. 

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5. Motion Segmentation via Generalized Curvatures

   https://doi.org/10.1109/TPAMI.2018.2869741  

   Arn, Robert; Narayana, Pradyumna; Draper, Bruce; Emerson, Tegan; Kirby, Michael; Peterson, Chris (September 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence)

   New depth sensors, like the Microsoft Kinect, produce streams of human pose data. These discrete pose streams can be viewed as noisy samples of an underlying continuous ideal curve that describes a trajectory through high-dimensional pose space. This paper introduces a technique for generalized curvature analysis (GCA) that determines features along the trajectory which can be used to characterize change and segment motion. Tools are developed for approximating generalized curvatures at mean points along a curve in terms of the singular values of local mean-centered data balls. The features of the GCA algorithm are illustrated on both synthetic and real examples, including data collected from a Kinect II sensor. We also applied GCA to the Carnegie Mellon University Motion Capture (MoCaP) database. Given that GCA scales linearly with the length of the time series we are able to analyze large data sets without down sampling. It is demonstrated that the generalized curvature approximations can be used to segment pose streams into motions and transitions between motions. The GCA algorithm can identify 94.2% of the transitions between motions without knowing the set of possible motions in advance, even though the subjects do not stop or pause between motions. 

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