P15-1006 lexical groundings with a rule-based scene generation model . The learned groundings
P15-1006 whether lexical grounding improves scene generation , we need a method to arrange
P15-1006 tems . Regrettably , the task of scene generation has not yet benefited from recent
P15-1006 Figure 2 ) . A naive approach to scene generation might use keyword search to retrieve
P15-1006 this model to build an improved scene generation system . Identifying object categories
D14-1217 learned priors are essential for scene generation . We also discuss interesting
P15-1006 approach that can approximate 3D scene generation . However , there are fundamental
D14-1217 next_to ( table , chair ) 6 Text to Scene generation We generate 3D scenes from brief
P15-1006 of lexical features for use in scene generation . This classifier learns from
E03-2002 ( Egges et al. , 2001 ) . The scene generation algorithm positions the static
D14-1217 approach to the task of text-to-3D scene generation . We present a representation
P15-1006 , prior work on the text to 3D scene generation task has used manually specified
P15-1006 quantitatively that our method improves 3D scene generation over previous work using purely
D14-1217 plausible results in the text - to-3D scene generation task . Spatial knowledge is critically
P15-1006 knowledge that is important for scene generation and not addressed by our learned
D14-1217 handling natural language for scene generation , by learning spatial knowledge
D14-1217 3D scene data . In text-to - 3D scene generation , a user provides as input natural
P15-1006 COMMIT / . <title> Text to 3D Scene Generation with Rich Lexical Grounding </title>
P15-1006 train and algorithmically evaluate scene generation systems that map descriptions
D14-1217 demonstrated the promise of text to scene generation systems but also pointed out
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