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Greyscale input for colourizing the plant scene. Pair with the colour ground truth in this group.
Sepia input for colourizing the plant scene. Pair with the colour ground truth in this group.
Colour ground-truth for the apple conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the cat conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the fruit conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the mug conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the plant conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the robot conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
Tangent-space surface-normal map encoded from the scene depth — a normal-conditioning input for guided generation and relighting tests.
Sparse scribble derived from the edges — the loose hand-drawn control input for scribble-guided image generation.
Semantic segmentation map (subject vs background) — the label reference for segmentation-conditioned generation and mask-quality scoring.
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
Tangent-space surface-normal map encoded from the scene depth — a normal-conditioning input for guided generation and relighting tests.
OpenPose-style keypoint skeleton for the figure — a pose-control input for guided generation and a target for pose-estimation checks.
Sparse scribble derived from the edges — the loose hand-drawn control input for scribble-guided image generation.
Semantic segmentation map (subject vs background) — the label reference for segmentation-conditioned generation and mask-quality scoring.
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
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