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Found 11 papers, 8 papers with code
Pippo: High-Resolution Multi-View Humans from a Single Image
Finally, we also introduce an improved metric to evaluate 3D consistency of multi-view generations, and show that Pippo outperforms existing works on multi-view human generation from a single image.
Sapiens: Foundation for Human Vision Models
We present Sapiens, a family of models for four fundamental human-centric vision tasks -- 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction.
Ranked #1 on
Keypoint Detection
on MS COCO
(Validation AP metric, using extra
training data)
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning.
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles.
Learning to Localize Through Compressed Binary Maps
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps.
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms.
LSQ++: Lower running time and higher recall in multi-codebook quantization
Multi-codebook quantization (MCQ) is the task of expressing a set of vectors as accurately as possible in terms of discrete entries in multiple bases.
A simple yet effective baseline for 3d human pose estimation
Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels.
Ranked #20 on
3D Human Pose Estimation
on HumanEva-I
On human motion prediction using recurrent neural networks
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality.
Stacked Quantizers for Compositional Vector Compression
Recently, Babenko and Lempitsky introduced Additive Quantization (AQ), a generalization of Product Quantization (PQ) where a non-independent set of codebooks is used to compress vectors into small binary codes.
3D Pose from Motion for Cross-view Action Recognition via Non-linear Circulant Temporal Encoding
We describe a new approach to transfer knowledge across views for action recognition by using examples from a large collection of unlabelled mocap data.
