Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Erickson R. Nascimento

Erickson R. Nascimento

Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method



Washington Ramos , Michel Silva , Edson Araujo , Victor Moura , Keller Oliveira , Leandro Soriano Marcolino , Erickson R. Nascimento

Figure 1 for Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
Figure 2 for Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
Figure 3 for Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
Figure 4 for Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
* Accepted to the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2022. arXiv admin note: text overlap with arXiv:2003.14229 
   Access Paper or Ask Questions

Learning Geodesic-Aware Local Features from RGB-D Images



Guilherme Potje , Renato Martins , Felipe Cadar , Erickson R. Nascimento

Figure 1 for Learning Geodesic-Aware Local Features from RGB-D Images
Figure 2 for Learning Geodesic-Aware Local Features from RGB-D Images
Figure 3 for Learning Geodesic-Aware Local Features from RGB-D Images
Figure 4 for Learning Geodesic-Aware Local Features from RGB-D Images
* This is a preprint version of the paper to appear at Computer Vision and Image Understanding (CVIU). The final journal version will be available at https://doi.org/10.1016/j.cviu.2022.103409 
   Access Paper or Ask Questions

Extracting Deformation-Aware Local Features by Learning to Deform



Guilherme Potje , Renato Martins , Felipe Cadar , Erickson R. Nascimento

Figure 1 for Extracting Deformation-Aware Local Features by Learning to Deform
Figure 2 for Extracting Deformation-Aware Local Features by Learning to Deform
Figure 3 for Extracting Deformation-Aware Local Features by Learning to Deform
Figure 4 for Extracting Deformation-Aware Local Features by Learning to Deform
* To appear in Proceedings of the Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS) 2021 
   Access Paper or Ask Questions

Creating and Reenacting Controllable 3D Humans with Differentiable Rendering



Thiago L. Gomes , Thiago M. Coutinho , Rafael Azevedo , Renato Martins , Erickson R. Nascimento

Figure 1 for Creating and Reenacting Controllable 3D Humans with Differentiable Rendering
Figure 2 for Creating and Reenacting Controllable 3D Humans with Differentiable Rendering
Figure 3 for Creating and Reenacting Controllable 3D Humans with Differentiable Rendering
Figure 4 for Creating and Reenacting Controllable 3D Humans with Differentiable Rendering
* 10 pages, 6 figures, to appear in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) 2022 
   Access Paper or Ask Questions

On Development and Evaluation of Retargeting Human Motion and Appearance in Monocular Videos



Thiago L. Gomes , Renato Martins , João Ferreira , Rafael Azevedo , Guilherme Torres , Erickson R. Nascimento

Figure 1 for On Development and Evaluation of Retargeting Human Motion and Appearance in Monocular Videos
Figure 2 for On Development and Evaluation of Retargeting Human Motion and Appearance in Monocular Videos
Figure 3 for On Development and Evaluation of Retargeting Human Motion and Appearance in Monocular Videos
Figure 4 for On Development and Evaluation of Retargeting Human Motion and Appearance in Monocular Videos
* 19 pages, 13 figures 
   Access Paper or Ask Questions

Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio



João P. Ferreira , Thiago M. Coutinho , Thiago L. Gomes , José F. Neto , Rafael Azevedo , Renato Martins , Erickson R. Nascimento

Figure 1 for Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio
Figure 2 for Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio
Figure 3 for Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio
Figure 4 for Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio
* Accepted at the Elsevier Computers & Graphics (C&G) 2020 
   Access Paper or Ask Questions

Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues



Renato Martins , Dhiego Bersan , Mario F. M. Campos , Erickson R. Nascimento

Figure 1 for Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues
Figure 2 for Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues
Figure 3 for Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues
Figure 4 for Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues
* Preprint version of the article to appear at Journal of Intelligent & Robotic Systems (2020) 
   Access Paper or Ask Questions

Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints



Thiago L. Gomes , Renato Martins , João Ferreira , Erickson R. Nascimento

Figure 1 for Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints
Figure 2 for Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints
Figure 3 for Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints
Figure 4 for Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints
* 10 pages, 8 figures, to appear in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) 2020 
   Access Paper or Ask Questions

Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network



Washington L. S. Ramos , Michel M. Silva , Edson R. Araujo , Alan C. Neves , Erickson R. Nascimento

Figure 1 for Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network
Figure 2 for Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network
Figure 3 for Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network
Figure 4 for Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network

   Access Paper or Ask Questions
1
2
>>