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TianNan Fu

dblp:58/7420 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visual content generation and editing · 77% Image and video processing · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visual content generation and editing › image retargeting
content-aware image resizing
0.112009
Real-time content-aware image resizing · Sci. China Ser. F Inf. Sci. 2009
Image and video processing
real-time image processing
0.012009
Real-time content-aware image resizing · Sci. China Ser. F Inf. Sci. 2009

Methods — techniques the papers use, named apart from their topics

seam carving · 0.1
YearPublicationVenuePosition
2011 Painterly rendering with content-dependent natural paint strokes
Hua Huang 0001, TianNan Fu, Chenfeng Li
Vis. Comput.2
2010 Video Painting via Motion Layer Manipulation
abstract
Abstract Temporal coherence is an important problem in Non‐Photorealistic Rendering for videos. In this paper, we present a novel approach to enhance temporal coherence in video painting. Instead of painting on video frame, our approach first partitions the video into multiple motion layers, and then places the brush strokes on the layers to generate the painted imagery. The extracted motion layers consist of one background layer and several object layers in each frame. Then, background layers from all the frames are aligned into a panoramic image, on which brush strokes are placed to paint the background in one‐shot. The strokes used to paint object layers are propagated frame by frame using smooth transformations defined by thin plate splines. Once the background and object layers are painted, they are projected back to each frame and blent to form the final painting results. Thanks to painting a single image, our approach can completely eliminate the flickering in background, and temporal coherence on object layers is also significantly enhanced due to the smooth transformation over frames. Additionally, by controlling the painting strokes on different layers, our approach is easy to generate painted video with multi‐style. Experimental results show that our approach is both robust and efficient to generate plausible video painting.
Hua Huang 0001, Lei Zhang 0021, TianNan Fu
Comput. Graph. Forum3
2009 Real-time content-aware image resizing
Hua Huang 0001, TianNan Fu, Paul L. Rosin, Chun Qi
Sci. China Ser. F Inf. Sci.2