Text2Scene-bib

A list of materials related to text2scene

Dataset investigation

• Abstract Scene Dataset (CVPR-2013 & PAMI-2015) #thorough

Abstract scene generation

• Learning the Visual Interpretation of Sentences (ICCV-2013)

  • Target: text -> Cartoon-like
  • Method: Statistical learning - Conditional Random Field (CRF)
  • Dataset: Abstract Scene Dataset
  • Supplementary Material

• Text2Scene: Generating Compositional Scenes from Textual Descriptions (CVPR-2019) #thorough

  • Target: text -> Cartoon-like scenes & Object layouts & Synthetic scenes
  • Method: End2end deep learning (Recurrent CNN + attention); Unified framework
  • Dataset: Abstract Scene Dataset; COCO
  • Code: Text2Scene

Learn commonsense spatiotemporal knowledge

• Predicting Object Dynamics in Scenes (CVPR-2014)

  • Target: scene -> next scene
  • Dataset: Abstract Scene Dataset

• Visual Abstraction for Zero-Shot Learning (ECCV-2014)

  • Target: learn concepts involving individual poses and interactions between two people
  • Dataset: Abstract scenes depicting fine-grained iteractions between two people
  • Webpage

• Learning common sense through visual abstraction (ICCV-2015)

  • Target: Assess the plausibility of the interaction in a scene
  • Dataset: Second Generation Abstract Scene Dataset
  • Webpage

3D scene generation

Stanford NLP group

• Learning Spatial Knowledge for Text to 3D Scene Generation (EMNLP-2014) #thorough

  • Target: Text -> 3D scene - room layout
  • Method: Mostly rule-based + Bayesian + NLP
  • Dataset: Collected dataset of spatial relation descriptions
  • Learned spatial relation mapping

• Interactive Learning of Spatial Knowledge for Text to 3D Scene Generation (ACL-2014-Workshop)

  • Target: Text -> 3D scene - room layout
  • Method: Interactive learning

• Text to 3D Scene Generation with Rich Lexical Grounding (ACL-2015) #thorough

  • Target: Text -> 3D scene - room layout
  • Method: Mostly rule-based + Supervised learning -> learning lexical grounding (i.e. object match)
  • Dataset: Collected dataset of scene-description pairs

3D shape generation

• Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings (CVPR-2018)
  • Target: text -> colored 3D shapes of tables and chairs
  • Method: Joint metric learning to capture many-to-many relations between text and properties of 3D shapes
  • Dataset: ShapeNet and manually collected text descriptions
  • Code: text2shape

Photorealistic image synthesis

GAN conditioned on text (Degrade on general images)

• Generative Adversarial Text to Image Synthesis (ICML-2016)

  • Target: text -> photographic image (Bird & flower)
  • Method: Both generator and discriminator conditioned on text feature
  • Dataset: CUB dataset of bird images; Oxford-102 dataset of flower images
  • Code: Generative Adversarial Text-to-Image Synthesis

• StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks (ICCV-2017)

  • Target: text -> photographic image (Bird & flower)
  • Method: two-stage GANs
  • Dataset: CUB; Oxford-102; MS-COCO
  • Code: StackGAN

Utilize pixel-wise semantic labels

• Parallel multiscale autoregressive density estimation (ICML-2017)

Semantic layout as intermediate representation OR Retrieval from oject database

• Semi-parametric Image Synthesis (CVPR-2018)

  • Target: Semantic layout -> Photographic image
  • Method:
    • Parametric + Non-parametric (segment retrieval)
    • Segment database -> retrieve -> composite -> resolve occlusion -> post-process
  • Dataset: Cityscapes; NYU; ADE20K (See Datasets in this paper)
  • Code: SIMS
  • Demo: Semi-parametric Image Synthesis

• Image Generation from Scene Graphs (CVPR-2018)

  • Target: Scene graphs -> Photographic images
  • Method:
    • groud-truth object positions -> scene graphs
    • Graph processing: graph convolution network
    • symbolic graph -> scene layout: bounding box & segmentation prediction
    • scene layout -> image: cascaded refinement network (CRN)
    • image -> realistic image: adversarial training
  • Dataset:
    • Visual Genome: Human annotated scene graphs provided
    • COCO-Stuff: COCO with pixel-level stuff annotations

• Inferring semantic layout for hierarchical text-to-image synthesis (CVPR-2018)

  • Target: text -> photographic image
  • Method: text -> semantic layout (box layout & shape) -> image
  • Dataset: COCO

Image query

• Image Ranking and Retrieval based on Multi-Attribute Queries (CVPR-2011)

• Image Retrieval Using Scene Graphs (CVPR-2015)

  • Target: Textual query -> Semantically related image
  • Method: Scene graph; Conditional random field
  • Dataset: real-world scene graphs: manually labeled YFCC100m & COCO images

Video generation

• Generating Videos with Scene Dynamics (NIPS-2016)

  • Target: video generation (unlabeled)
  • Method: Scene decomposition model: Foreground + Background + Mask (GAN*3)
  • Dataset: A large amount of unlabeled video downloaded from Flickr
  • Code: videoGAN

• Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks (NIPS-2016)

  • Target: frame -> next frame
  • Method: Probabilistic

• To Create What You Tell: Generating Videos from Captions (ACM-2017)

  • Target: caption -> video
  • Method: Conditional GAN: LSTM caption encoder + Convolutional generator + 3 discriminator conditioned on the caption
  • Dataset:
    • Synthesized videos of handwritten digits bouncing
    • Video snippets from YouTube about cooking

• Imagine This! Scripts to Compositions to Videos (ECCV-2018)

  • Target: text -> scene video
  • Method: Entity & Background retrieval + Layout composer
  • Dataset: FLINTSTONES: richly-annotated video-caption dataset
  • Demo: CRAFT

• Video Generation from Text (AAAI-2018)

  • Target: text -> video
  • Method: text -> gist -> video (VAE + GAN)
  • Dataset: Videos crawled from YouTube along with titles and descriptions

• MoCoGAN: Decomposing Motion and Content for Video Generation (CVPR-2018)

  • Target: video generation
  • Method: Content + motion GAM

• TFGAN: IMPROVING CONDITIONING FOR TEXT-TO-VIDEO SYNTHESIS (2018) #Withdrawn

• Generating Animated Videos of Human Activities from Natural Language Descriptions (NIPS-2018)

  • Target: text -> a sequence of 3D human skeletal poses
  • Method:
    • Autoencoder: Learn a representation of human motions without text
    • Seq2seq: map text into motion representation
  • Dataset: The KIT Motion-Language Dataset

• Language2Pose: Natural Language Grounded Pose Forecasting (2019)

  • Target: text -> pose animation
  • Method: learn a joint embedding of text and pose using curriculum learning
  • Dataset: The KIT Motion-Language Dataset

Visual story telling

• A Pipeline for Creative Visual Storytelling (2018)

  • Target: video -> a sequence of text (Pipeline proposed)

• Video Storytelling (2018)

  • Target: video -> a sequence of coherent and succinct text
  • Method:
    • Contextual multimodal embedding: Residual Bidirectional RNN
    • Narrator: Reinforcement learning