Changing color and marker of each point using seaborn jointplot

You can also directly precise it in the list of arguments, thanks to the keyword : joint_kws (tested with seaborn 0.8.1). If needed, you can also change the properties of the marginal with marginal_kws

So your code becomes :

import seaborn as sns
colors = np.random.random((len(tips),3))
markers = (['x','o','v','^','<']*100)[:len(tips)]

sns.jointplot("total_bill", "tip", data=tips, kind="reg",
    joint_kws={"color":colors, "marker":markers})

Solving this problem is almost no different than that from matplotlib (plotting a scatter plot with different markers and colors), except I wanted to keep the marginal distributions:

import seaborn as sns
from itertools import product
sns.set(style="darkgrid")

tips = sns.load_dataset("tips")
color = sns.color_palette()[5]
g = sns.jointplot("total_bill", "tip", data=tips, kind="reg", stat_func=None,
                  xlim=(0, 60), ylim=(0, 12), color='k', size=7)

#Clear the axes containing the scatter plot
g.ax_joint.cla()

#Generate some colors and markers
colors = np.random.random((len(tips),3))
markers = ['x','o','v','^','<']*100

#Plot each individual point separately
for i,row in enumerate(tips.values):
    g.ax_joint.plot(row[0], row[1], color=colors[i], marker=markers[i])

g.set_axis_labels('total bill', 'tip', fontsize=16)

Which gives me this:

The regression line is now gone, but this is all I needed.

The accepted answer is too complicated. plt.sca() can be used to do this in a simpler way:

import matplotlib.pyplot as plt
import seaborn as sns

tips = sns.load_dataset("tips")
g = sns.jointplot("total_bill", "tip", data=tips, kind="reg", stat_func=None,
                  xlim=(0, 60), ylim=(0, 12))


g.ax_joint.cla() # or g.ax_joint.collections[0].set_visible(False), as per mwaskom's comment

# set the current axis to be the joint plot's axis
plt.sca(g.ax_joint)

# plt.scatter takes a 'c' keyword for color
# you can also pass an array of floats and use the 'cmap' keyword to
# convert them into a colormap
plt.scatter(tips.total_bill, tips.tip, c=np.random.random((len(tips), 3)))