# BlockGAN – Supplemental material and results

BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images
Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang and Niloy Mitra
NeurIPS 2020

➤ Supplemental video (MP4)
➤ Supplemental document (PDF)
➤ Results on the CLEVR n datasets (n = 2, 3, 4)
➤ Results on the Synth-Car n datasets (n = 1, 2, 3)
➤ Results on the Synth-Chair n datasets (n = 1, 2, 3)
➤ Results on the natural Real-Cars dataset

## Results on the CLEVR n datasets (n = 2, 3, 4) 🔝

### Rotating objects

### Horizontal translation

### Translation along depth

### Editing the background with fixed foreground (varying z0 with fixed z1)

### Editing the foreground with fixed background (varying z1 with fixed z0)

## Results on the Synth-Car n datasets (n = 1, 2, 3) 🔝

### Rotating objects

### Horizontal translation

### Translation along depth

### Editing the background with fixed foreground (varying z0 with fixed z1)

### Editing the foreground with fixed background (varying z1 with fixed z0)

## Results on the Synth-Chair n datasets (n = 1, 2, 3) 🔝

### Rotating objects

### Horizontal translation

### Translation along depth

### Editing the background with fixed foreground (varying z0 with fixed z1)

### Editing the foreground with fixed background (varying z1 with fixed z0)

## Results on the natural Real-Cars dataset 🔝

### Rotating objects

### Horizontal translation

### Editing the background with fixed foreground (varying z0 with fixed z1)

### Editing the foreground with fixed background (varying z1 with fixed z0)

### Adding objects (Real-Cars dataset)

### Stretching objects (Real-Cars dataset)

### Slicing objects (Real-Cars dataset)