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20162023
most citedNeural Volumes: Learning Dynamic Renderable Volumes from Images

680 citations · 2.5k across the 34 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cs.CV2019

To React or not to React: End-to-End Visual Pose Forecasting for Personalized Avatar during Dyadic Conversations

Chaitanya Ahuja, Shugao Ma, Louis-Philippe Morency +1

Non verbal behaviours such as gestures, facial expressions, body posture, and para-linguistic cues have been shown to complement or clarify verbal messages. Hence to improve telepr…

cs.CV2019★ 13 cited

Single-Network Whole-Body Pose Estimation

Gines Hidalgo, Yaadhav Raaj, Haroon Idrees +4

We present the first single-network approach for 2D~whole-body pose estimation, which entails simultaneous localization of body, face, hands, and feet keypoints. Due to the bottom-…

cs.GR2019★ 680 cited

Neural Volumes: Learning Dynamic Renderable Volumes from Images

Stephen Lombardi, Tomas Simon, Jason Saragih +3

Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain complex phenomena such as thin structures, evolving topology, translucency, scattering, occ…

cs.CV2019★ 1 cited

Shapes and Context: In-the-Wild Image Synthesis & Manipulation

Aayush Bansal, Yaser Sheikh, Deva Ramanan

We introduce a data-driven approach for interactively synthesizing in-the-wild images from semantic label maps. Our approach is dramatically different from recent work in this spac…

cs.CV2019

Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction

Hanbyul Joo, Tomas Simon, Mina Cikara +1

We present a new research task and a dataset to understand human social interactions via computational methods, to ultimately endow machines with the ability to encode and decode a…

cs.CV2019★ 1 cited

LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds

Chun-Liang Li, Tomas Simon, Jason Saragih +2

We present LBS-AE; a self-supervised autoencoding algorithm for fitting articulated mesh models to point clouds. As input, we take a sequence of point clouds to be registered as we…